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Louis Wehenkel - Publications ORBI
Marcos Alvarez, A., Louveaux, Q., & Wehenkel, L. (2017). A Machine Learning-Based Approximation of Strong Branching. INFORMS Journal on Computing, 29(1), 185-195.
Peer reviewed (verified by ORBi)
We present in this paper a new generic approach to variable branching in branch-and-bound for mixed- integer linear problems. Our approach consists in imitating the decisions taken by a good branching strategy ...
Duchesne, L., Karangelos, E., & Wehenkel, L. (2017). Machine Learning of Real-time Power Systems Reliability Management Response. PowerTech Manchester 2017 Proceedings.
Peer reviewed
In this paper we study how supervised machine learning could be applied to build simplified models of real-time (RT) reliability management response to the realization of uncertainties. The final objective is ...
Marin, M., Karangelos, E., & Wehenkel, L. (2017). A computational model of mid-term outage scheduling for long-term system studies. PowerTech Manchester 2017 Proceedings.
Peer reviewed
This paper presents a computational model of the mid-term outage scheduling process of electric power transmis- sion assets, to be used in long-term studies such as mainte- nance policy assessments and ...
Wehenkel, L. (2016, November 09). Big data, machine learning, and optimization, for power systems reliability. Paper presented at Louis Wehenkel, Belval, Luxembourg.
How to combine physical models with observational data for ensuring power systems reliability, by leveraging simulation, optimisation, and machine learning.
Sun, H., Zhao, F., Wang, H., Wang, K., Jiang, W., Guo, Q., Zhang, B., & Wehenkel, L. (2016). Automatic learning of fine operating rules for online power system security control. IEEE Transactions on Neural networks and learning systems, 27(8), 1708-1719.
Peer reviewed
Fine operating rules for security control and an automatic system for their online discovery were developed to adapt to the development of smart grids. The automatic system uses the real-time system state to ...
Karangelos, E., & Wehenkel, L. (2016). Probabilistic reliability management approach and criteria for power system real-time operation. Power Systems Computation Conference.
Peer reviewed
This paper develops a probabilistic approach for power system reliability management in real-time operation where risk is a product of i) the potential occurrence of contingencies, ii) the possible failure of ...
Perkin, S., Bjornsson, G., Baldursdottir, I., Palsson, M., Kristjansson, R., Stefansson, H., Jensson, P., Karangelos, E., & Wehenkel, L. (2016). Framework for Threat Based Failure Rates in Transmission System Operation. Framework for Threat Based Failure Rates in Transmission System Operation.
Peer reviewed
Reliability of electrical transmission systems isvpresently managed by applying the deterministic N-1 criterion, or some variant thereof. This means that transmission systems are designed with at least one ...
Marée, R., Rollus, L., Stévens, B., Hoyoux, R., Louppe, G., Vandaele, R., Begon, J.-M., Kainz, P., Geurts, P., & Wehenkel, L. (2016, January 10). Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics, 7.
Peer reviewed (verified by ORBi)
Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of ...
Marcos Alvarez, A., Wehenkel, L., & Louveaux, Q. (2016). Online Learning for Strong Branching Approximation in Branch-and-Bound. Eprint/Working paper retrieved from http://orbi.ulg.ac.be/handle/2268/192361.
We present an online learning approach to variable branching in branch-and-bound for mixed-integer linear problems. Our approach consists in learning strong branching scores in an online fashion and in using ...
Marée, R., Geurts, P., & Wehenkel, L. (2016). Towards Generic Image Classification using Tree-based Learning: an Extensive Empirical Study. Pattern Recognition Letters.
Peer reviewed (verified by ORBi)
This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common ...
Van Lishout, F., Gadaleta, F., Moore, J. H., Wehenkel, L., & Van Steen, K. (2015). gammaMAXT: a fast multiple-testing correction algorithm. BioData Mining, 8(36).
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Background: The purpose of the maxT algorithm is to provide a significance test algorithm that controls the family-wise error rate (FWER) during simultaneous hypothesis testing. However, the requirements in ...
Schrynemackers, M., Wehenkel, L., Madan Babu, M., & Geurts, P. (2015). Classifying pairs with trees for supervised biological network inference. Molecular Biosystems, 11(8), 2116-2125.
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Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known ...
Aceto, J., Nourizadeh-Lillabadi, R., Marée, R., Dardenne, N., Jeanray, N., Wehenkel, L., Aleström, P., van Loon, J., & Muller, M. (2015). Zebrafish bone and general physiology are differently affected by hormones or changes in gravity. PLoS ONE, 10(6), 1-42.
Peer reviewed (verified by ORBi)
Teleost fish such as zebrafish (Danio rerio) are increasingly used for physiological, genetic and developmental studies. Our understanding of the physiological consequences of altered gravity in an entire ...
Jeanray, N., Marée, R., Pruvot, B., Stern, O., Geurts, P., Wehenkel, L., & Muller, M. (2015). Phenotype Classification of Zebrafish Embryos by Supervised Learning. PLoS ONE, 10(1), 0116989, 1-20.
Peer reviewed (verified by ORBi)
Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on ...
Marcos Alvarez, A., Wehenkel, L., & Louveaux, Q. (2015). Machine Learning to Balance the Load in Parallel Branch-and-Bound. Eprint/Working paper retrieved from http://orbi.ulg.ac.be/handle/2268/181086.
We describe in this paper a new approach to parallelize branch-and-bound on a certain number of processors. We propose to split the optimization of the original problem into the optimization of several ...
Marée, R., Geurts, P., & Wehenkel, L. (2014). Towards generic image classification: an extensive empirical study (1). Eprint/Working paper retrieved from http://orbi.ulg.ac.be/handle/2268/175525.
This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common ...
Vincke, G., Marée, R., Wehenkel, L., Defaweux, V., QUATRESOOZ, P., d'Haene, N., Salmon, I., Renard, P., Depiereux, E., Snoeck, C., Denis, B., & Verpoorten, D. (2014, December 16). HistoWeb - Toward a new learning ecology for histology. Paper presented at Digital learning round table, Luxembourg, Luxembourg.
HistoWeb targets the transformation of the professional tool Cytomineinto a comprehensive and innovative teaching platform, valuing the notions of learning ecology and new learning dimensions seeking for ...
Van Lishout, F., Gadaleta, F., Moore, J. H., Wehenkel, L., & Van Steen, K. (2014). gammaMAXT: a fast multiple-testing correction algorithm. ERCIM 2014 Abstract Book (pp. 47).
Peer reviewed
The purpose of the maxT algorithm (1993) is to control the family-wise error rate (FWER) when assessing significance of multiple tests jointly. However, the requirements in terms of computing time and memory ...
Wang, D., Glavic, M., & Wehenkel, L. (2014). Trajectory-Based Supplementary Damping Control for Power System Electromechanical Oscillations. IEEE Transactions on Power Systems, 29(6), 2835-2845.
Peer reviewed (verified by ORBi)
This paper considers a trajectory-based approach to determine control signals superimposed to those of existing controllers so as to enhance the damping of electromechanical oscillations. This approach is ...
Joly, A., Geurts, P., & Wehenkel, L. (2014). Random forests with random projections of the output space for high dimensional multi-label classification. Machine Learning and Knowledge Discovery in Databases.
Peer reviewed
We adapt the idea of random projections applied to the out- put space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be ...
Capitanescu, F., & Wehenkel, L. (2014). An AC OPF-based Heuristic Algorithm for Optimal Transmission Switching. Proceedings of the 18th Power Systems Computation Conference (pp. 1-6).
Peer reviewed
This paper focuses on reducing generators dispatch cost by means of transmission line switching. The problem is formulated as a mixed-integer nonlinear program (MINLP) optimal power flow (OPF). A scalable ...
Panciatici, P., Campi, M. C., Garatti, S., Low, S. H., Molzahn, D. K., Sun, A. X., & Wehenkel, L. (2014). Advanced optimization methods for power systems. Proceedings of the 18th Power Systems Computation Conference (pp. 1-18).
Power system planning and operation offers multitudinous opportunities for optimization methods. In practice, these problems are generally large-scale, non-linear, subject to uncertainties, and combine both ...
Wang, D., Glavic, M., & Wehenkel, L. (2014). Comparison of centralized, distributed and hierarchical model predictive control schemes for electromechanical oscillations damping in large-scale power systems. International Journal of Electrical Power & Energy Systems, 58, 32-41.
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The paper investigates the feasibility of applying Model Predictive Control (MPC) as a viable strategy to damp wide-area electromechanical oscillations in large-scale power systems. First a fully ... ...
Marée, R., Rollus, L., Stevens, B., Louppe, G., Caubo, O., Rocks, N., Bekaert, S., Cataldo, D., & Wehenkel, L. (2014). A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning. Proceedings IEEE International Symposium on Biomedical Imaging. IEEE.
Peer reviewed
We present a novel methodology combining web-based software development practices, machine learning, and spatial databases for computer-aided quantification of regions of interest (ROIs) in large-scale ...
Platbrood, L., Capitanescu, F., Crisciu, H., & Wehenkel, L. (2014). A generic approach for solving nonlinear-discrete security-constrained power flow problems in large-scale systems. IEEE Transactions on Power Systems, 29(3), 1194-1203.
Peer reviewed (verified by ORBi)
This paper proves the practicality of an iterative algorithm for solving realistic large-scale SCOPF problems. This algorithm is based on the combination of a contingency filtering scheme, used to identify the ...
Botta, V., Louppe, G., Geurts, P., & Wehenkel, L. (2014, April 02). Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies. PLoS ONE.
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The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however, are ...
Jung, T., Wehenkel, L., Ernst, D., & Maes, F. (2014). Optimized look-ahead tree policies: a bridge between look-ahead tree policies and direct policy search. International Journal of Adaptive Control and Signal Processing, 28(3-5), 255-289.
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Direct policy search (DPS) and look-ahead tree (LT) policies are two popular techniques for solving difficult sequential decision-making problems. They both are simple to implement, widely applicable without ...
Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (2014). Apprentissage par renforcement batch fondé sur la reconstruction de trajectoires artificielles. Proceedings of the 9èmes Journées Francophones de Planification, Décision et Apprentissage (JFPDA 2014).
Peer reviewed
Cet article se situe dans le cadre de l’apprentissage par renforcement en mode batch, dont le problème central est d’apprendre, à partir d’un ensemble de trajectoires, une politique de décision optimisant un ...
Marcos Alvarez, A., Louveaux, Q., & Wehenkel, L. (2014). A Supervised Machine Learning Approach to Variable Branching in Branch-And-Bound. Eprint/Working paper retrieved from http://orbi.ulg.ac.be/handle/2268/167559.
We present in this paper a new approach that uses supervised machine learning techniques to improve the performances of optimization algorithms in the context of mixed-integer programming (MIP). We focus on ...
Ruiz-Vega, D., Wehenkel, L., Ernst, D., Pizano-Martinez, A., & Fuerte-Esquivel, C. (2014). Power system transient stability preventive and emergency control. In S., Savulescu (Ed.), Real-Time Stability in Power Systems 2nd Edition (pp. 123-158). Springer.
A general approach to real-time transient stability control is described, yielding various complementary techniques: pure preventive, open loop emergency, and closed loop emergency controls. Recent progress in ...
Becker, J., Maes, F., & Wehenkel, L. (2013, December 16). On the Encoding of Proteins for Disordered Regions Prediction. PLoS ONE.
Peer reviewed (verified by ORBi)
Disordered regions, i.e., regions of proteins that do not adopt a stable three-dimensional structure, have been shown to play various and critical roles in many biological processes. Predicting and ...
Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P. (2013). Understanding variable importances in forests of randomized trees. Advances in Neural Information Processing Systems 26.
Peer reviewed
Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view. In this work we ...
Capitanescu, F., & Wehenkel, L. (2013). Computation of worst operation scenarios under uncertainty for static security management. IEEE Transactions on Power Systems, 28(2), 1697-1705.
Peer reviewed (verified by ORBi)
This paper deals with day-ahead static security assessment with respect to a postulated set of contingencies while taking into account uncertainties about the next day system conditions. We propose a ...
Van Lishout, F., Mahachie John, J., Gusareva, E., Urrea, V., Cleynen, I., Theatre, E., Charloteaux, B., Calle, M. L., Wehenkel, L., & Van Steen, K. (2013). An efficient algorithm to perform multiple testing in epistasis screening. BMC Bioinformatics, 14, 138.
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Background: Research in epistasis or gene-gene interaction detection for human complex traits has grown over the last few years. It has been marked by promising methodological developments, improved ...
Becker, J., Maes, F., & Wehenkel, L. (2013). On the Relevance of Sophisticated Structural Annotations for Disulfide Connectivity Pattern Prediction. PLoS ONE, 8(2), 56621.
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Disulfide bridges strongly constrain the native structure of many proteins and predicting their formation is therefore a key sub-problem of protein structure and function inference. Most recently proposed ...
Marée, R., Wehenkel, L., & Geurts, P. (2013). Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval. In A., Criminisi & J., Shotton (Eds.), Decision Forests in Computer Vision and Medical Image Analysis, Advances in Computer Vision and Pattern Recognition (pp. 125-142). Springer.
Peer reviewed
We present a unified framework involving the extraction of random subwindows within images and the induction of ensembles of extremely randomized trees. We discuss the specialization of this framework for ...
Capitanescu, F., & Wehenkel, L. (2013). Experiments with the interior-point method for solving large scale Optimal Power Flow problems. Electric Power Systems Research, 95, 276–283.
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This paper reports extensive results obtained with the interior-point method (IPM) for nonlinear programmes (NLPs) stemming from large-scale and severely constrained classical Optimal Power Flow (OPF) and ...
Defourny, B., Ernst, D., & Wehenkel, L. (2013). Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning. INFORMS Journal on Computing, 25(3), 488-501.
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In the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector-valued ...
Fliscounakis, S., Panciatici, P., Capitanescu, F., & Wehenkel, L. (2013). Contingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive, and corrective actions. IEEE Transactions on Power Systems.
Peer reviewed (verified by ORBi)
This paper deals with day-ahead security management with respect to a postulated set of contingencies, while taking into account uncertainties about the next day generation/load scenario. In order to help the ...
Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (2013). Stratégies d'échantillonnage pour l'apprentissage par renforcement batch. Revue d'Intelligence Artificielle [=RIA], 27(2), 171-194.
Peer reviewed (verified by ORBi)
We propose two strategies for experiment selection in the context of batch mode reinforcement learning. The first strategy is based on the idea that the most interesting experiments to carry out at some stage ...
Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2013). Gene regulatory network inference from systems genetics data using tree-based methods. In A., de la Fuente (Ed.), Gene Network Inference - Verification of Methods for Systems Genetics Data (pp. 63-85). Springer.
Peer reviewed
One of the pressing open problems of computational systems biology is the elucidation of the topology of gene regulatory networks (GRNs). In an attempt to solve this problem, the idea of systems genetics is to ...
Karangelos, E., Panciatici, P., & Wehenkel, L. (2013). Whither probabilistic security management for real-time operation of power systems ? Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP), 2013 IREP Symposium. IEEE.
Peer reviewed
This paper investigates the stakes of introducing probabilistic approaches for the management of power system’s security. In real-time operation, the aim is to arbitrate in a rational way between preventive ...
Maes, F., Wehenkel, L., & Ernst, D. (2013). Meta-learning of Exploration/Exploitation Strategies: The Multi-Armed Bandit Case. In J., Filipe & A., Fred (Eds.), Agents and Artificial Intelligence: 4th International Conference, ICAART 2012, Vilamoura, Portugal, February 6-8, 2012. Revised Selected Papers (pp. 110-115). Springer.
Peer reviewed
The exploration/exploitation (E/E) dilemma arises naturally in many subfields of Science. Multi-armed bandit problems formalize this dilemma in its canonical form. Most current research in this field focuses on ...
Van Lishout, F., Vens, C., Urrea, V., Calle, M. L., Wehenkel, L., & Van Steen, K. (2012, December 03). Survival analysis: finding relevant epistatic SNP pairs using Model- Based Multifactor Dimensionality Reduction. Paper presented at 5th International Conference of the ERCIM WG on COMPUTING & STATISTICS, Oviedo, Spain.
Peer reviewed
Analyzing the combined effects of genes (and/or environmental factors) on the development of complex diseases is quite challenging, both from the statistical and computational perspective, even using a ...
Capitanescu, F., Fliscounakis, S., Panciatici, P., & Wehenkel, L. (2012). Cautious operation planning under uncertainties. IEEE Transactions on Power Systems, 27(4), 1859-1869.
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This paper deals with day-ahead power systems security planning under uncertainties, by posing an optimization problem over a set of power injection scenarios that could show up the next day and modeling the ...
Maes, F., Fonteneau, R., Wehenkel, L., & Ernst, D. (2012). Policy search in a space of simple closed-form formulas: towards interpretability of reinforcement learning. Discovery Science 15th International Conference, DS 2012, Lyon, France, October 29-31, 2012. Proceedings (pp. 37-51). Berlin, Germany: Springer.
Peer reviewed
In this paper, we address the problem of computing interpretable solutions to reinforcement learning (RL) problems. To this end, we propose a search algorithm over a space of simple losed-form formulas that ...
Maes, F., Geurts, P., & Wehenkel, L. (2012). Embedding Monte Carlo search of features in tree-based ensemble methods. In P., Flach, T., De Bie, & N., Cristianini (Eds.), Machine Learning and Knowledge Discovery in Data Bases (pp. 191-206). Springer.
Peer reviewed
Feature generation is the problem of automatically constructing good features for a given target learning problem. While most feature generation algorithms belong either to the filter or to the wrapper ...
Schnitzler, F., Geurts, P., & Wehenkel, L. (2012). Mixtures of Bagged Markov Tree Ensembles. In A., Cano Utrera, M., Gómez-Olmedo, & T., Nielsen (Eds.), Proceedings of the 6th European Workshop on Probabilistic Graphical Models (pp. 283-290).
Peer reviewed
Markov trees, a probabilistic graphical model for density estimation, can be expanded in the form of a weighted average of Markov Trees. Learning these mixtures or ensembles from observations can be performed ...
Hiard, S., Geurts, P., & Wehenkel, L. (2012). Comparator selection for RPC with many labels. ECAI 2012 : 20th European Conference on Artificial Intelligence : 27-31 August 2012, Montpellier, France (pp. 408-413). Amsterdam, Netherlands: IOS Press.
Peer reviewed
The Ranking by Pairwise Comparison algorithm (RPC) is a well established label ranking method. However, its complexity is of O(N²) in the number N of labels. We present algorithms for selection, before model ...
Marano Marcolini, A., Capitanescu, F., Jose Luis, M. R., & Wehenkel, L. (2012). Exploiting the use of DC SCOPF approximation to improve iterative AC SCOPF algorithms. IEEE Transactions on Power Systems, 27(3), 1459-1466.
Peer reviewed (verified by ORBi)
This paper focuses on improving the solution techniques for the AC SCOPF problem of active power dispatch by using the DC SCOPF approximation within the SCOPF algorithm. Our approach brings two benefits ...
Joly, A., Schnitzler, F., Geurts, P., & Wehenkel, L. (2012). L1-based compression of random forest models. Proceeding of the 21st Belgian-Dutch Conference on Machine Learning.
Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive ...
Schnitzler, F., Ammar, S., Leray, P., Geurts, P., & Wehenkel, L. (2012). Approximation efficace de mélanges bootstrap d’arbres de Markov pour l’estimation de densité. In L., Bougrain (Ed.), Actes de la 14e Conférence Francophone sur l'Apprentissage Automatique (CAp 2012) (pp. 207-222).
Peer reviewed
Nous considérons des algorithmes pour apprendre des Mélanges bootstrap d'Arbres de Markov pour l'estimation de densité. Pour les problèmes comportant un grand nombre de variables et peu d'observations, ces ...
Huynh-Thu, V. A., Saeys, Y., Wehenkel, L., & Geurts, P. (2012). Statistical interpretation of machine learning-based feature importance scores for biomarker discovery. Bioinformatics, 28(13), 1766-1774.
Peer reviewed (verified by ORBi)
Motivation: Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only ...
Joly, A., Schnitzler, F., Geurts, P., & Wehenkel, L. (2012). L1-based compression of random forest models. 20th European Symposium on Artificial Neural Networks.
Peer reviewed
Random forests are effective supervised learning methods applicable to large-scale datasets. However, the space complexity of tree ensembles, in terms of their total number of nodes, is often prohibitive ...
Maes, F., Wehenkel, L., & Ernst, D. (2012). Learning to play K-armed bandit problems. Proceedings of the 4th International Conference on Agents and Artificial Intelligence (ICAART 2012).
Peer reviewed
We propose a learning approach to pre-compute K-armed bandit playing policies by exploiting prior information describing the class of problems targeted by the player. Our algorithm first samples a set of K ...
Schrouff, J., Kussé, C., Wehenkel, L., Maquet, P., & Phillips, C. (2012). Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes. PLoS ONE, 7(4).
Peer reviewed (verified by ORBi)
Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental ...
Joly, A., Schnitzler, F., Geurts, P., & Wehenkel, L. (2011, November 29). Pruning randomized trees with L1-norm regularization. Poster session presented at DYSCO Study Day, Leuven-Heverlee, Belgium.
Growing amount of high dimensional data requires robust analysis techniques. Tree-based ensemble methods provide such accurate supervised learning models. However, the model complexity can become utterly huge ...
Schrouff, J., Kussé, C., Wehenkel, L., Maquet, P., & Phillips, C. (2011, November 22). Decoding semi-constrained brain activity from fMRI using SVM and GP. Paper presented at I Workshop on Mathematical Modeling and Computational Neuroscience of UFABC, São Paulo, Brazil.
Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental ...
Wang, D., Glavic, M., & Wehenkel, L. (2011). Distributed MPC of wide-area electromechanical oscillations of large-scale power systems. Proceedings of ISAP 2011.
Peer reviewed
We investigate distributed Model Predictive Control (MPC) to damp wide-area electromechanical oscillations. Our distributed MPC schemes are derived from and compared with a fully centralized MPC scheme ...
Capitanescu, F., Fliscounakis, S., Panciatici, P., & Wehenkel, L. (2011). Day-ahead Security Assessment under Uncertainty Relying on the Combination of Preventive and Corrective Controls to Face Worst-Case Scenarios. PSCC proceedings Stockholm (Sweden) 2011.
Peer reviewed
This paper deals with day-ahead static security assessment with respect to a postulated set of contingencies while taking into account uncertainties about the next day system conditions. We propose a ...
Capitanescu, F., Martinez Ramos, J. L., Panciatici, P., Kirschen, D., Marano Marcolini, A., Platbrood, L., & Wehenkel, L. (2011). State-of-the-art, challenges, and future trends in security constrained optimal power flow. Electric Power Systems Research, 81(8), 1731-1741.
Peer reviewed (verified by ORBi)
This paper addresses the main challenges to the security constrained optimal power flow (SCOPF) computations. We first discuss the issues related to the SCOPF problem formulation such as the use of a limited ...
Capitanescu, F., & Wehenkel, L. (2011). Redispatching active and reactive powers using a limited number of control actions. IEEE Transactions on Power Systems, 26(3), 1221-1230.
Peer reviewed (verified by ORBi)
This paper deals with some essential open questions in the field of optimal power flow (OPF) computations, namely: the limitation of the number of controls allowed to move, the trade-off between the ...
Platbrood, L., Crisciu, H., Capitanescu, F., & Wehenkel, L. (2011). SOLVING VERY LARGE-SCALE SECURITY-CONSTRAINED OPTIMAL POWER FLOW PROBLEMS BY COMBINING ITERATIVE CONTINGENCY SELECTION AND NETWORK COMPRESSION. PSCC conference.
Peer reviewed
This paper proposes a practical algorithm for solving very large-scale SCOPF problems, based on the combination of a contingency filtering scheme, used to identify the binding contingencies at the optimum, and ...
Schnitzler, F., & Wehenkel, L. (2011, June 29). Two-level Mixtures of Markov Trees. Poster session presented at The 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Belfast, Ireland.
Peer reviewed
We study algorithms for learning Mixtures of Markov Trees for density estimation. There are two approaches to build such mixtures, which both exploit the interesting scaling properties of Markov Trees. We ...
Levels, J. H., Geurts, P., Karlsson, H., Marée, R., Ljunggren, S., Fornander, L., Wehenkel, L., Lindahl, M., Stroes, E. S., Kuivenhoven, J. A., & Meijers, J. C. (2011). High-density lipoprotein proteome dynamics in human endotoxemia. Proteome science, 9(1), 34.
Peer reviewed (verified by ORBi)
BACKGROUND: A large variety of proteins involved in inflammation, coagulation, lipid-oxidation and lipid metabolism have been associated with high-density lipoprotein (HDL) and it is anticipated that changes ...
Schrouff, J., Kussé, C., Wehenkel, L., Maquet, P., & Phillips, C. (2011, June 26). Decoding Directed Brain Activity in fMRI using Support Vector Machines and Gaussian Processes. Poster session presented at Organization of Human Brain Mapping, Québec, Canada.
Peer reviewed
Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental ...
Hoffmann, R., Promel, F., Capitanescu, F., Krost, G., & Wehenkel, L. (2011). Situation Adapted Display of Information for Operating Very Large Interconnected Grids. Power Tech Conference.
Peer reviewed
This paper addresses the problem of security monitoring and situation awareness in very large interconnected transmission systems, with particular emphasis on the continental European grid. An innovative ...
Wang, D., Glavic, M., & Wehenkel, L. (2011). A new MPC scheme for damping wide-area electromechanical oscillations in power systems. the 2011 IEEE PES PowerTech.
Peer reviewed
This paper introduces a new Model Predictive Control (MPC) scheme to damp wide-area electromechanical oscillations. The proposed MPC controller, based on a linearized discrete-time state space model ...
Stern, O., Marée, R., Aceto, J., Jeanray, N., Muller, M., Wehenkel, L., & Geurts, P. (2011, May 20). Zebrafish Skeleton Measurements using Image Analysis and Machine Learning Methods. Poster session presented at Belgian Dutch Conference on Machine learning (Benelearn).
Peer reviewed
The zebrafish is a model organism for biological studies on development and gene function. Our work aims at automating the detection of the cartilage skeleton and measuring several distances and angles to ...
Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2011). Active exploration by searching for experiments that falsify the computed control policy. Proceedings of the 2011 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL-11).
Peer reviewed
We propose a strategy for experiment selection - in the context of reinforcement learning - based on the idea that the most interesting experiments to carry out at some stage are those that are the most liable ...
Schnitzler, F., Geurts, P., & Wehenkel, L. (2011, March 21). Looking for applications of mixtures of Markov trees in bioinformatics. Paper presented at BioMAGNet Annual Meeting 2011, Bruxelles, Belgium.
Probabilistic graphical models (PGM) efficiently encode a probability distribution on a large set of variables. While they have already had several successful applications in biology, their poor scaling in ...
Hiard, S., & Wehenkel, L. (2011). Using Class-probability Models instead of Hard Classifiers as Base Learners in the Ranking by Pairwise Comparison Algorithm. In S., Thatcher (Ed.), ICMLC 2011 3rd International Conference on Machine Learning and Computing Volume 1 (pp. 218-222). Chengdu, China: IEEE.
In the field of Preference Learning, the Ranking by Pairwise Comparison algorithm (RPC) consists of using the learning sample to derive pairwise comparators for each possible pair of class labels, and then ...
DE SENY, D., Sharif, M., Fillet, M., COBRAIVILLE, G., Meuwis, M.-A., Marée, R., HAUZEUR, J.-P., Wehenkel, L., Louis, E., Merville, M.-P., Kirwan, J., Ribbens, C., & Malaise, M. (2011). Discovery and biochemical characterisation of four novel biomarkers for osteoarthritis. Annals of the Rheumatic Diseases, 70(6), 1144-52.
Peer reviewed (verified by ORBi)
OBJECTIVE: Knee osteoarthritis (OA) is a heterogeneous, complex joint pathology of unknown aetiology. Biomarkers have been widely used to investigate OA but currently available biomarkers lack specificity and ...
Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2011). Towards min max generalization in reinforcement learning. In J., Filipe, A., Fred, & B., Sharp (Eds.), Agents and Artificial Intelligence: International Conference, ICAART 2010, Valencia, Spain, January 2010, Revised Selected Papers (pp. 61-77). Springer.
Peer reviewed
In this paper, we introduce a min max approach for addressing the generalization problem in Reinforcement Learning. The min max approach works by determining a sequence of actions that maximizes the worst ...
Lamrini, B, L. E. K., & Wehenkel, L. (2011). Data validation and missing data reconstruction using self-organizing map for water treatment. Neural Computing & Applications, 20(4), 575-588.
Peer reviewed (verified by ORBi)
Applications in the water treatment domain generally rely on complex sensors located at remote sites. The processing of the corresponding measurements for generating higher-level information such as ...
Maes, F., Becker, J., & Wehenkel, L. (2011). Iterative multi-task sequence labeling for predicting structural properties of proteins. ESANN 2011.
Peer reviewed
Developing computational tools for predicting protein structural information given their amino acid sequence is of primary importance in protein science. Problems, such as the prediction of secondary ...
Maes, F., Becker, J., & Wehenkel, L. (2011). Prédiction structurée multitâche itérative de propriétés structurelles de protéines. 7e Plateforme AFIA: Association Française pour l'Intelligence Artificielle (pp. 279). Editions Publibook.
Peer reviewed
Le développement d'outils informatiques pour prédire de l'information structurelle de protéines à partir de la séquence en acides aminés constitue un des défis majeurs de la bioinformatique. Les problèmes tels ...
Maes, F., Wehenkel, L., & Ernst, D. (2011). Automatic discovery of ranking formulas for playing with multi-armed bandits. Proceedings of the 9th European Workshop on Reinforcement Learning (EWRL 2011).
Peer reviewed
We propose an approach for discovering in an automatic way formulas for ranking arms while playing with multi-armed bandits. The approach works by de ning a grammar made of basic elements such as for example ...
Maes, F., Wehenkel, L., & Ernst, D. (2011). Optimized look-ahead tree policies. Proceedings of the 9th European Workshop on Reinforcement Learning (EWRL 2011).
Peer reviewed
We consider in this paper look-ahead tree techniques for the discrete-time control of a deterministic dynamical system so as to maximize a sum of discounted rewards over an in finite time horizon. Given the ...
Rachelson, E., Schnitzler, F., Wehenkel, L., & Ernst, D. (2011). Optimal sample selection for batch-mode reinforcement learning. Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART 2011).
Peer reviewed
We introduce the Optimal Sample Selection (OSS) meta-algorithm for solving discrete-time Optimal Control problems. This meta-algorithm maps the problem of finding a near-optimal closed-loop policy to the ...
Stern, O., Marée, R., Aceto, J., Jeanray, N., Muller, M., Wehenkel, L., & Geurts, P. (2011). Automatic localization of interest points in zebrafish images with tree-based methods. Proceedings of the 6th IAPR International Conference on Pattern Recognition in Bioinformatics. Springer.
Peer reviewed
In many biological studies, scientists assess effects of experimental conditions by visual inspection of microscopy images. They are able to observe whether a protein is expressed or not, if cells are going ...
Capitanescu, F., & Wehenkel, L. (2010). Sensitivity-based approaches for handling discrete variables in optimal power flow computations. IEEE Transactions on Power Systems, 25(4), 1780-1789.
Peer reviewed (verified by ORBi)
This paper proposes and compares three iterative approaches for handling discrete variables in optimal power flow (OPF) computations. The first two approaches rely on the sensitivities of the objective and ...
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2010). Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS ONE, 5(9), 12776.
Peer reviewed (verified by ORBi)
One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene ...
Ammar, S., Leray, P., Schnitzler, F., & Wehenkel, L. (2010). Sub-quadratic Markov tree mixture learning based on randomizations of the Chow-Liu algorithm. In P., Myllymäki, A., Roos, & T., Jaakkola (Eds.), Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010) (pp. 17-24).
Peer reviewed
The present work analyzes different randomized methods to learn Markov tree mixtures for density estimation in very high-dimensional discrete spaces (very large number n of discrete variables) when the ...
Fonteneau, F., Ernst, D., Druet, C., Panciatici, P., & Wehenkel, L. (2010). Consequence driven decomposition of large-scale power system security analysis. Proceedings of the 2010 IREP Symposium - Bulk Power Systems Dynamics and Control - VIII.
Peer reviewed
This paper presents an approach for assessing, in operation planning studies, the security of a large-scale power system by decomposing it into elementary subproblems, each one corresponding to a structural ...
Panciatici, P., Hassaine, T., Fliscounakis, S., Platbrood, L., Ortega-Vazquez, M.-A., Martinez-Ramos, J. L., & Wehenkel, L. (2010). Security management under uncertainty: From day-ahead planning to intraday operation. Proceedings of Bulk Power System Dynamics and Control (iREP) - VIII (iREP), 2010 iREP Symposium.
Peer reviewed
In this paper, we propose to analyse the practical task of dealing with uncertainty for security management by Transmission System Operators in the context of day-ahead planning and intraday operation. We ...
Pisane, J., Marée, R., Wehenkel, L., & Verly, J. (2010, May 24). Radar Classification based on Extra-Trees.
Peer reviewed
In this paper, we describe a new automatic target recognition algorithm for classifying SAR images based on the PiXiT im- age classifier. It uses randomized sub-windows extraction and extremely randomized ...
Schnitzler, F., Leray, P., & Wehenkel, L. (2010, May 10). Vers un apprentissage subquadratique pour les mélanges d’arbres. Paper presented at 5èmes Journées Francophones sur les Réseaux Bayésiens, Nantes, France.
Peer reviewed
We consider randomization schemes of the Chow-Liu algorithm from weak (bagging, of quadratic complexity) to strong ones (full random sampling, of linear complexity), for learn- ing probability density ...
Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2010). Generating informative trajectories by using bounds on the return of control policies. Proceedings of the Workshop on Active Learning and Experimental Design 2010 (in conjunction with AISTATS 2010).
Peer reviewed
We propose new methods for guiding the generation of informative trajectories when solving discrete-time optimal control problems. These methods exploit recently published results that provide ways for ...
Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2010). Model-free Monte Carlo-like policy evaluation. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010) (pp. 217-224).
Peer reviewed
We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards along ...
Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2010). Model-free Monte Carlo–like policy evaluation. Proceedings of Conférence Francophone sur l'Apprentissage Automatique (CAp) 2010.
Peer reviewed
We propose an algorithm for estimating the finite-horizon expected return of a closed loop control policy from an a priori given (off-policy) sample of one-step transitions. It averages cumulated rewards along ...
Schnitzler, F., Leray, P., & Wehenkel, L. (2010, April). Towards sub-quadratic learning of probability density models in the form of mixtures of trees.
Peer reviewed
We consider randomization schemes of the Chow-Liu algorithm from weak (bagging, of quadratic complexity) to strong ones (full random sampling, of linear complexity), for learning probability density models in ...
Marée, R., Denis, P., Wehenkel, L., & Geurts, P. (2010). Incremental Indexing and Distributed Image Search using Shared Randomized Vocabularies. ACM Proceedings MIR 2010.
Peer reviewed
We present a cooperative framework for content-based image retrieval for the realistic setting where images are distributed across multiple cooperating servers. The proposed method is in line ...
Capitanescu, F., & Wehenkel, L. (2010). Optimal power flow computations with a limited number of controls allowed to move. IEEE Transactions on Power Systems, 25(1), 586-587.
Peer reviewed (verified by ORBi)
This letter focuses on optimal power flow (OPF) computations in which no more than a pre-specified number of controls are allowed to move. To determine an efficient subset of controls satisfying this ...
Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2010). A cautious approach to generalization in reinforcement learning. Proceedings of the 2nd International Conference on Agents and Artificial Intelligence (pp. 10).
Peer reviewed
In the context of a deterministic Lipschitz continuous environment over continuous state spaces, finite action spaces, and a finite optimization horizon, we propose an algorithm of polynomial complexity which ...
Fonteneau, R., Murphy, S. A., Wehenkel, L., & Ernst, D. (2010). Computing bounds for kernel-based policy evaluation in reinforcement learning. University of Liège.
This technical report proposes an approach for computing bounds on the finite-time return of a policy using kernel-based approximators from a sample of trajectories in a continuous state space and deterministic framework.
Pisane, J., Marée, R., Wehenkel, L., & Verly, J. (2010). Robust Automatic Target Recognition Using Extra-trees. In J., Pisane (Ed.), Robust Automatic Target Recognition Using Extra-trees. IEEE.
Peer reviewed
In this paper, we describe a new automatic target recognition algorithm for classifying SAR images based on the PiXiT image classifier. It uses randomized sub-windows extraction and extremely randomized trees ...
Cornélusse, B., Geurts, P., & Wehenkel, L. (2009, December 12). Tree based ensemble models regularization by convex optimization. Paper presented at NIPS-09 workshop on Optimization for Machine Learning, Whistler, Canada.
Peer reviewed
Tree based ensemble methods can be seen as a way to learn a kernel from a sample of input-output pairs. This paper proposes a regularization framework to incorporate non-standard information not used in the ...
Defourny, B., & Wehenkel, L. (2009, December). Large Margin Classification with the Progressive Hedging Algorithm. Paper presented at Second NIPS Workshop on Optimization for Machine Learning, Whistler, Canada.
Peer reviewed
Several learning algorithms in classification and structured prediction are formulated as large scale optimization problems. We show that a generic iterative reformulation and resolving strategy based on the ...
Geurts, P., Irrthum, A., & Wehenkel, L. (2009). Supervised learning with decision tree-based methods in computational and systems biology. Molecular Biosystems, 5(12), 1593-1605.
Peer reviewed (verified by ORBi)
At the intersection between artificial intelligence and statistics, supervised learning provides algorithms to automatically build predictive models only from observations of a system. During the last ...
Capitanescu, F., Rosehart, W., & Wehenkel, L. (2009). Optimal power flow computations with constraints limiting the number of control actions. IEEE Power Tech conference.
Peer reviewed
This paper focuses on optimal power flow (OPF) computations in which no more than a pre-specified number of controls are allowed to move. The benchmark formulation of this OPF problem constitutes a mixed ...
Capitanescu, F., & Wehenkel, L. (2009). A new heuristic approach to deal with discrete variables in optimal power flow computations. IEEE Power Tech conference.
Peer reviewed
This paper proposes a new heuristic approach to deal with discrete variables in an optimal power flow (OPF). This approach relies on the first order sensitivity of the objective and ...
Fabozzi, D., Glavic, M., Wehenkel, L., & Van Cutsem, T. (2009). Security Assessment by Multiple Transmission System Operators Exchanging Sensitivity and Tie-Line Power Flow Information. Proceedings of the 2009 IEEE PES Power Tech conference.
Peer reviewed
This paper considers a procedure for multi-area static security assessment of large interconnected power systems operated by a team of Transmission System Operators (TSOs). In this procedure, each TSO provides ...
Capitanescu, F., Van Cutsem, T., & Wehenkel, L. (2009). Coupling optimization and dynamic simulation for preventive-corrective control of voltage instability. IEEE Transactions on Power Systems, 24(2), 796 - 805.
Peer reviewed (verified by ORBi)
This paper proposes an approach coupling security constrained optimal power flow with time-domain simulation to determine an optimal combination of preventive and corrective controls ensuring a voltage stable ...
Dumont, M., Marée, R., Wehenkel, L., & Geurts, P. (2009). Fast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees. Proc. International Conference on Computer Vision Theory and Applications (VISAPP) (pp. 196-203).
Peer reviewed
This paper addresses image annotation, i.e. labelling pixels of an image with a class among a finite set of predefined classes. We propose a new method which extracts a sample of subwindows from a set of ...
Marée, R., Geurts, P., & Wehenkel, L. (2009). Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees. IPSJ Transactions on Computer Vision and Applications, 1.
Peer reviewed
We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly extracted ...
Belmudes, F., Ernst, D., & Wehenkel, L. (2009). Pseudo-geographical representations of power system buses by multidimensional scaling. Proceedings of the 15th International Conference on Intelligent System Applications to Power Systems (ISAP 2009).
Peer reviewed
Graphical representations of power systems are systematically used for planning and operation. The coordinate systems commonly used by Transmission System Operators are static and reflect the geographical ...
Belmudes, F., Ernst, D., & Wehenkel, L. (2009). A rare-event approach to build security analysis tools when N-k (k > 1) analyses are needed (as they are in large-scale power systems). Proceedings of the 2009 IEEE Bucharest PowerTech.
Peer reviewed
We consider the problem of performing N − k security analyses in large scale power systems. In such a context, the number of potentially dangerous N − k contingencies may become rapidly very large when k grows ...
Cornélusse, B., Vignal, G., Defourny, B., & Wehenkel, L. (2009). Supervised learning of intra-daily recourse strategies for generation management under uncertainties. PowerTech, 2009 IEEE Bucharest (pp. 1-8).
Peer reviewed
The aim of this work is to design intra-daily recourse strategies which may be used by operators to decide in real-time the modifications to bring to planned generation schedules of a set of units in order to ...
De Seny, D., Ribbens, C., Cobraiville, G., Meuwis, M.-A., Marée, R., Geurts, P., Wehenkel, L., Louis, E., Merville, M.-P., Fillet, M., & Malaise, M. (2009). Protéomique par SELDI-TOF-MS des maladies inflammatoires articulaires: identification des protéines S100 comme protéines d'intérêt. Revue Médicale de Liège, 64(Spec No), 29-35.
Peer reviewed (verified by ORBi)
Clinical proteomics is a technical approach studying the entire proteome expressed by cells, tissues or organs. It describes the dynamics of cell regulation by detecting molecular events related ...
Defourny, B., Ernst, D., & Wehenkel, L. (2009). Bounds for Multistage Stochastic Programs using Supervised Learning Strategies. In O., Watanabe & T., Zeugmann (Eds.), Stochastic Algorithms: Foundations and Applications (pp. 61-73). Springer.
Peer reviewed
We propose a generic method for obtaining quickly good upper bounds on the minimal value of a multistage stochastic program. The method is based on the simulation of a feasible decision policy, synthesized by ...
Defourny, B., Ernst, D., & Wehenkel, L. (2009). Planning under uncertainty, ensembles of disturbance trees and kernelized discrete action spaces. Proceedings of the IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL-09) (pp. 145-152).
Peer reviewed
Optimizing decisions on an ensemble of incomplete disturbance trees and aggregating their first stage decisions has been shown as a promising approach to (model-based) planning under uncertainty in large ...
Ernst, D., Glavic, M., Capitanescu, F., & Wehenkel, L. (2009). Reinforcement learning versus model predictive control: a comparison on a power system problem. IEEE Transactions on Systems, Man & Cybernetics : Part B, 33(2), 517-519.
Peer reviewed (verified by ORBi)
This paper compares reinforcement learning (RL) with model predictive control (MPC) in a unified framework and reports experimental results of their application to the synthesis of a controller for a nonlinear ...
Ernst, D., Wehenkel, L., & Pavella, M. (2009). What is the likely future of real-time transient stability ? Proceedings of the 2009 IEEE/PES Power Systems Conference & Exposition (PSCE 2009).
Peer reviewed
Despite very intensive research efforts in the field of transient stability during the last five decades, the large majority of the derived techniques have hardly moved from the research laboratories to the ...
Fonteneau, R., Murphy, S., Wehenkel, L., & Ernst, D. (2009). Inferring bounds on the performance of a control policy from a sample of trajectories. Proceedings of the IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL-09) (pp. 117-123).
Peer reviewed
We propose an approach for inferring bounds on the finite-horizon return of a control policy from an off-policy sample of trajectories collecting state transitions, rewards, and control actions. In this paper ...
Quesada Calvo, F., Fillet, M., De Seny, D., Meuwis, M.-A., Marée, R., Crahay, C., Paulissen, G., Rocks, N., Guéders, M., Wehenkel, L., Merville, M.-P., Louis, R., Foidart, J.-M., Noël, A., & Cataldo, D. (2009). Biomarker discovery in asthma-related inflammation and remodeling. Proteomics, 9(8), 2163-2170.
Peer reviewed (verified by ORBi)
Asthma is a complex inflammatory disease of airways. A network of reciprocal interactions between inflammatory cells, peptidic mediators, extracellular matrix components, and proteases is thought to be ...
Schnitzler, F., & Wehenkel, L. (2009). Constraint Based Learning of Mixtures of Trees. Paper presented at Probabilistic graphical models for integration of complex data and discovery of causal models in biology, Nantes, France.
Peer reviewed
Mixtures of trees can be used to model any multivariate distributions. In this work the possibility to learn these models from data by causal learning is explored. The algorithm developed aims at ...
Auvray, V., & Wehenkel, L. (2008). Learning parameters in discrete naive Bayes models by computing fibers of the parametrization map. NIPS ´08 Workshop: Algebraic and combinatorial methods in machine learning.
Peer reviewed
Discrete Naive Bayes models are usually defined parametrically with a map from a parameter space to a probability distribution space. First, we present two families of algorithms that compute the set of ...
Stubbe, M., Karoui, K., Van Cutsem, T., & Wehenkel, L. (2008, December). Le projet PEGASE. Revue E Tijdschrift, (4), 37-41.
Peer reviewed
A group of Transmission System Operators (TSO’s), expert companies and leading research centers in power system analysis and applied mathematics, under the coordination of Tractebel Engineering, has joined to ...
Wehenkel, L., Ernst, D., Rousseaux, P., & Van Cutsem, T. (2008, December). Research and Education Activities in Electric Power Systems at the University of Liège. Revue E Tijdschrift, (4), 54-59.
Peer reviewed
This paper presents research and education activities of the power systems group of the Department of Electrical Engineering and Computer Science of the University of Liège. These activities cover power ...
Capitanescu, F., & Wehenkel, L. (2008). A new iterative approach to the corrective security-constrained optimal power flow problem. IEEE Transactions on Power Systems, 23(4), 1342-1351.
Peer reviewed (verified by ORBi)
This paper deals with techniques to solve the corrective security-constrained optimal power flow (CSCOPF) problem. To this end, we propose a new iterative approach that comprises four modules: a CSCOPF ...
Botta, V., Hansoul, S., Geurts, P., & Wehenkel, L. (2008). Raw genotypes vs haplotype blocks for genome wide association studies by random forests. Proc. of MLSB 2008, second workshop on Machine Learning in Systems Biology.
Peer reviewed
We consider two different representations of the input data for genome-wide association studies using random forests, namely raw genotypes described by a few thousand to a few hundred thousand discrete ...
Auvray, V., & Wehenkel, L. (2008). Learning inclusion-optimal chordal graphs. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI-08) (pp. 18–25). Morgan Kaufmann.
Peer reviewed
Chordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the chordal ...
Kelner, V., Capitanescu, F., Léonard, O., & Wehenkel, L. (2008). A hybrid optimization technique coupling evolutionary and local search algorithms. Journal of Computational & Applied Mathematics, 215(2), 448-456.
Peer reviewed (verified by ORBi)
Evolutionary Algorithms are robust and powerful global optimization techniques for solving large scale problems that have many local optima. However, they require high CPU times, and they are very poor in ...
Ali, M., Glavic, M., Buisson, J., Wehenkel, L., & Ernst, D. (2008). Analyzing transient instability phenomena beyond the classical stability boundary. Proceedings of the 40th North American Power Symposium (NAPS 2008).
Peer reviewed
We consider power systems for which the amount of power produced by their individual power plants is small with respect to the total generation of the system, and analyze how the transient instability ...
Ammar, S., Leray, P., & Wehenkel, L. (2008). Estimation de densité par ensemble aléatoire de poly-arbres.
Peer reviewed
La notion de mélange de modèles simples aléatoires est de plus en plus utilisée et avec succès dans la littérature de l’apprentissage supervisé ces dernières années. Parmi les avantages de ces méthodes ...
Belmudes, F., Ernst, D., & Wehenkel, L. (2008). Cross-entropy based rare-event simulation for the identification of dangerous events in power systems. Proceedings of the 10th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS-08).
Peer reviewed
We propose in this paper a novel approach for identifying rare events that may endanger power system integrity. This approach is inspired by the rare-event simulation literature and, in particular, by the ...
De Seny, D., Fillet, M., Ribbens, C., Marée, R., Meuwis, M.-A., Lutteri, L., Chapelle, J.-P., Wehenkel, L., Louis, E., Merville, M.-P., & Malaise, M. (2008). Monomeric calgranulins measured by SELDI-TOF mass spectrometry and calprotectin measured by ELISA as biomarkers in arthritis. Clinical Chemistry, 54, 1066-1075.
Peer reviewed (verified by ORBi)
BACKGROUND: SELDI-TOF mass spectrometry (MS) is a high-throughput proteomic approach with potential for identifying novel forms of serum biomarkers of arthritis. METHODS: We used SELDI-TOF MS to ...
Defourny, B., Ernst, D., & Wehenkel, L. (2008). Lazy planning under uncertainty by optimizing decisions on an ensemble of incomplete disturbance trees. In B., Defourny, D., Ernst, & L., Wehenkel, Recent Advances in Reinforcement Learning (pp. 1-14).
Peer reviewed
This paper addresses the problem of solving discrete-time optimal sequential decision making problems having a disturbance space W composed of a finite number of elements. In this context, the problem of ...
Defourny, B., Ernst, D., & Wehenkel, L. (2008). Risk-aware decision making and dynamic programming. Paper presented at NIPS-08 Workshop on Model Uncertainty and Risk in Reinforcement Learning, Whistler, Canada.
Peer reviewed
This paper considers sequential decision making problems under uncertainty, the tradeoff between the expected return and the risk of high loss, and methods that use dynamic programming to find optimal policies ...
Fonteneau, R., Wehenkel, L., & Ernst, D. (2008). Variable selection for dynamic treatment regimes: a reinforcement learning approach. Paper presented at European Workshop on Reinforcement Learning 2008 (EWRL'08), Villeneuve d'Ascq, France.
Peer reviewed
Dynamic treatment regimes (DTRs) can be inferred from data collected through some randomized clinical trials by using reinforcement learning algorithms. During these clinical trials, a large set of clinical ...
Irrthum, A., & Wehenkel, L. (2008). Predicting gene essentiality from expression patterns in Escherichia coli.
Peer reviewed
Essential genes are genes whose loss of function causes lethal- ity. In the case of pathogen organisms, the identification of these genes is of considerable interest, as they provide targets for the ...
Meuwis, M.-A., Fillet, M., Lutteri, L., Marée, R., Geurts, P., De Seny, D., Malaise, M., Chapelle, J.-P., Wehenkel, L., Belaiche, J., Merville, M.-P., & Louis, E. (2008). Proteomics for prediction and characterization of response to infliximab in Crohn's disease: a pilot study. Clinical Biochemistry, 41(12), 960-7.
Peer reviewed (verified by ORBi)
OBJECTIVES: Infliximab is the first anti-TNFalpha accepted by the Food and Drug Administration for use in inflammatory bowel disease treatment. Few clinical, biological and genetic factors tend to predict ...
Hiard, S., Rigali, S., Colson, S., Marée, R., & Wehenkel, L. (2007, November 12). PREDetector : Prokaryotic Regulatory Element Detector. Poster session presented at Benelux Bioinformatics Conference 2007, Leuven, Belgium.
Background: In the post-genomic area, in silico predictions of regulatory networks are considered as a powerful approach to decipher and understand biological pathways within prokaryotic cells. The emergence ...
Capitanescu, F., Glavic, M., Ernst, D., & Wehenkel, L. (2007). Contingency filtering techniques for preventive security-constrained optimal power flow. IEEE Transactions on Power Systems, 22(4), 1690-1697.
Peer reviewed (verified by ORBi)
This paper focuses on contingency filtering to accelerate the iterative solution of preventive security-constrained optimal power flow (PSCOPF) problems. To this end, we propose two novel filtering techniques ...
Del Angel, A., Geurts, P., Ernst, D., Glavic, M., & Wehenkel, L. (2007). Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities. Neurocomputing, 70(16-18), 2668-2678.
Peer reviewed (verified by ORBi)
This paper investigates a possibility for estimating rotor angles in the time frame of transient (angle) stability of electric power systems, for use in real-time. The proposed dynamic state estimation ...
Huynh-Thu, V. A., Hiard, S., Geurts, P., Muller, M., Struman, I., Martial, J., & Wehenkel, L. (2007, September). Detection of micro-RNA/gene interactions involved in angiogenesis using machine learning techniques. Poster session presented at Workshop on Machine Learning in Systems Biology (MLSB07), Evry, France.
Motivation: Angiogenesis is the process responsible for the growth of new blood vessels from existing ones. It is also associated with the development of cancer, as tumors need to be irrigated by blood vessels ...
Mack, P., Capitanescu, F., Glavic, M., Legrand, F., & Wehenkel, L. (2007, July). Application of the Galileo system for a better synchronization of electrical power systems.
Peer reviewed
In this paper we present objectives and strategies of the NAVELEC research project funded by the European Union Galileo Joint Undertaking (GJU/05/2423). The project objective is to assess how the European ...
Hiard, S., Marée, R., Colson, S., Hoskisson, P. A., Titgemeyer, F., van Wezel, G. P., Joris, B., Wehenkel, L., & Rigali, S. (2007). PREDetector: A new tool to identify regulatory elements in bacterial genomes. Biochemical and Biophysical Research Communications, 357(4), 861-864.
Peer reviewed (verified by ORBi)
In the post-genomic area, the prediction of transcription factor regulons by position weight matrix-based programmes is a powerful approach to decipher biological pathways and to modelize regulatory networks ...
Capitanescu, F., & Wehenkel, L. (2007). Improving the statement of the corrective security-constrained optimal power flow problem. IEEE Transactions on Power Systems, 22(2), 887-889.
Peer reviewed (verified by ORBi)
This letter proposes a formulation of the corrective security-constrained optimal power-flow problem imposing, in addition to the classical post-contingency constraints, existence and viability constraints on ...
Capitanescu, F., Glavic, M., Ernst, D., & Wehenkel, L. (2007). Interior-point based algorithms for the solution of optimal power flow problems. Electric Power Systems Research, 77(5-6), 508-517.
Peer reviewed (verified by ORBi)
Interior-point method (IPM) is a very appealing approach to the optimal power flow (OPF) problem mainly due to its speed of convergence and ease of handling inequality constraints. This paper analyzes the ...
Defourny, B., & Wehenkel, L. (2007, March). Projecting Generation Decisions Induced by a Stochastic Program on a Family of Supply Curve Functions. Paper presented at Third Carnegie Mellon Conference on the Electricity Industry, Pittsburgh, Pennsylvania, USA.
We propose to post-process the results of a scenario based stochastic program by projecting its decisions on a parameterized space of policies. By doing so the risk of overfitting to the set of scenarios used ...
Hiard, S., Rigali, S., Colson, S., Marée, R., & Wehenkel, L. (2007, February 15). PREDetector : Prokaryotic Regulatory Element Detector. Poster session presented at Bioinformatics and Modeling : From genomes to network, Liège, Belgium.
Background: In the post-genomic area, in silico predictions of regulatory networks are considered as a powerful approach to decipher and understand biological pathways within prokaryotic cells. The emergence ...
Chandrika, K., Dellot, P., Frippiat, F., Giot, J.-B., Leonard, P., Marée, R., Mayasi, N., Meuris, C., Mukeba Tshialala, D., Rahmouni, S., Uurlings, F., Vaira, D., Wehenkel, L., Demonty, J., & Moutschen, M. (2007). Nouvelles approches dans la prise en charge de l'infection a VIH. Revue Médicale de Liège, 62 Spec No, 47-50.
Peer reviewed (verified by ORBi)
HIV infection remains a major problem of public health in Belgium as well as globally. The number of new diagnosies of HIV infection in Belgium remains between two and three daily. Given the dramatic effect of ...
Cornélusse, B., Wehenkel, L., & Wera, C. (2007). Automatic learning for the classification of primary frequency control behaviour. Power Tech, 2007 IEEE Lausanne (pp. 273-278).
Peer reviewed
In this paper we propose a methodology based on supervised automatic learning in order to classify the behaviour of generators in terms of their performance in providing primary frequency control ancillary ...
Ernst, D., Glavic, M., Capitanescu, F., & Wehenkel, L. (2007). Model predictive control and reinforcement learning as two complementary frameworks. International Journal of Tomography & Statistics, 6, 122-127.
Peer reviewed (verified by ORBi)
Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a discrete ...
Ernst, D., Glavic, M., Stan, G.-B., Mannor, S., & Wehenkel, L. (2007). The cross-entropy method for power system combinatorial optimization problems. Proceedings of the 2007 Power Tech (pp. 1290-1295).
Peer reviewed
We present an application of a cross-entropy based combinatorial optimization method for solving some unit commitment problems. We report simulation results and analyze, under several perspectives (accuracy ...
Glavic, M., Ernst, D., Ruiz-Vega, D., Wehenkel, L., & Pavella, M. (2007). E-SIME- A method for transient stability closed-loop emergency control: achievements and prospects. Proceedings of 2007 IREP Symposium - Bulk Power Systems Dynamics and Control - VII.
Peer reviewed
A general response-based technique is presented for closed-loop transient stability emergency control. It relies on E-SIME, derived from the hybrid transient stability method, SIME. E-SIME uses real-time ...
Marée, R., Geurts, P., & Wehenkel, L. (2007). Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees. Proc. 8th Asian Conference on Computer Vision (ACCV), LNCS (pp. 611-620). Springer-Verlag.
Peer reviewed
We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly ...
Marée, R., Geurts, P., & Wehenkel, L. (2007). Random subwindows and extremely randomized trees for image classification in cell biology. BMC Cell Biology, 8(Suppl. 1).
Peer reviewed (verified by ORBi)
Background: With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation ...
Meuwis, M.-A., Fillet, M., Geurts, P., De Seny, D., Lutteri, L., Chapelle, J.-P., Bours, V., Wehenkel, L., Belaiche, J., Malaise, M., Louis, E., & Merville, M.-P. (2007). Biomarker discovery for inflammatory bowel disease, using proteomic serum profiling. Biochemical Pharmacology, 73(9), 1422-1433.
Peer reviewed (verified by ORBi)
Crohn's disease and ulcerative colitis known as inflammatory bowel diseases (IBD) are chronic immuno-inflammatory pathologies of the gastrointestinal tract. These diseases are multifactorial, polygenic and of ...
Wehenkel, L., Glavic, M., & Ernst, D. (2007). A collaborative framework for multi-area dynamic security assessment of large scale systems. Proceedings of the 2007 Power Tech (pp. 261-266).
Peer reviewed
In this paper we propose a collaborative framework to carry out multi-area dynamic security assessment over an interconnection operated by a team of TSOs responsible of different areas. In this framework each ...
Hiard, S., Rigali, S., Colson, S., Marée, R., & Wehenkel, L. (2006, May 17). PreDetector : Prokaryotic Regulatory Element Detector. Poster session presented at 10th Bioforum, Liège, Belgium.
PreDetector is a stand-alone software, written in java. Its final aim is to predict regulatory sites for prokaryotic species. It comprises two functionalities. The first one is very similar to Target ...
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3-42.
Peer reviewed (verified by ORBi)
This paper proposes anew tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while splitting a ...
Capitanescu, F., Glavic, M., Ernst, D., & Wehenkel, L. (2006). Applications of security-constrained optimal power flows. In Proceedings of Modern Electric Power Systems Symposium, MEPS06.
Peer reviewed
This paper proposes to formulate security control as a sequential decision making problem and presents new developments in automatic learning of sequential decision making strategies from simulations and/or ...
Ernst, D., Glavic, M., Capitanescu, F., & Wehenkel, L. (2006). Model predictive control and reinforcement learning as two complementary frameworks. Proceedings of the 13th IFAC Workshop on Control Applications of Optimisation.
Peer reviewed
Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a discrete ...
Ernst, D., Marée, R., & Wehenkel, L. (2006). Reinforcement learning with raw image pixels as input state. Advances in machine vision, image processing & pattern analysis (Lecture notes in computer science, Vol. 4153). Berlin: Springer-Verlag Berlin.
Peer reviewed
We report in this paper some positive simulation results obtained when image pixels are directly used as input state of a reinforcement learning algorithm. The reinforcement learning algorithm chosen to carry ...
Ernst, D., Stan, G.-B., Gonçalves, J., & Wehenkel, L. (2006). Clinical data based optimal STI strategies for HIV: a reinforcement learning approach. Proceedings of the 45th IEEE Conference on Decision and Control (CDC 2006) (pp. 667 - 672).
Peer reviewed
This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies ...
Ernst, D., Stan, G.-B., Gonçalves, J., & Wehenkel, L. (2006). Clinical data based optimal STI strategies for HIV: a reinforcement learning approach. Proceedings of the 15th Annual Machine Learning Conference of Belgium and The Netherlands (Benelearn 2006).
Peer reviewed
This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies ...
Geurts, P., Marée, R., & Wehenkel, L. (2006). Segment and combine: a generic approach for supervised learning of invariant classifiers from topologically structured data. Proceedings of the Machine Learning Conference of Belgium and The Netherlands (Benelearn) (pp. 15-23).
Peer reviewed
A generic method for supervised classification of structured objects is presented. The approach induces a classifier by (i) deriving a surrogate dataset from a pre-classified dataset of structured objects ...
Geurts, P., Wehenkel, L., & d Alché-Buc, F. (2006). Kernelizing the output of tree-based methods. Proceedings of the 23rd International Conference on Machine Learning (pp. 345--352). Acm.
Peer reviewed
We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the output space. The resulting ...
Glavic, M., Wehenkel, L., & Ernst, D. (2006). Damping control by fusion of reinforcement learning and control Lyapunov functions. Proceedings of the 38th North American Power Symposium (NAPS 2006) (pp. 361-367).
Peer reviewed
The main idea behind the concept, proposed in the paper, is the opportunity to make control systems with increased capabilities by synergetic fusion of the domain-specific knowledge and the methodologies from ...
Kelner, V., Capitanescu, F., Léonard, O., & Wehenkel, L. (2006). A hybrid optimization technique coupling an evolutionary and a local search algorithm. Journal of Computational & Applied Mathematics, 215(2), 281-287.
Peer reviewed (verified by ORBi)
Evolutionary algorithms are robust and powerful global optimization techniques for solving large-scale problems that have many local optima. However, they require high CPU times, and they are very poor in ...
Marée, R., Geurts, P., & Wehenkel, L. (2006). Biological Image Classification with Random Subwindows and Extra-Trees. Paper presented at Bio-Image Informatics (Workshop on Multiscale Biological Imaging, Data Mining & Informatics), Santa Barbara, USA.
Peer reviewed
We illustrate the potential of our image classification method on three datasets of images at different imaging modalities/scales, from subcellular locations up to human body regions. The method is based on ...
Wehenkel, L., Ernst, D., & Geurts, P. (2006). Ensembles of extremely randomized trees and some generic applications. Proceedings of Robust Methods for Power System State Estimation and Load Forecasting.
Peer reviewed
In this paper we present a new tree-based ensemble method called “Extra-Trees”. This algorithm averages predictions of trees obtained by partitioning the inputspace with randomly generated splits, leading to ...
Wehenkel, L., Glavic, M., & Ernst, D. (2006). Multi-area security assessment: results using efficient bounding method. Proceedings of the 38th North American Power Symposium (NAPS 2006) (pp. 511-515).
Peer reviewed
We present our recent results on using previously introduced framework for multi-area security assessment in large interconnections. The basic idea of the framework is exchanging just enough information so ...
Wehenkel, L., Glavic, M., & Ernst, D. (2006). On multi-area security assessment of large interconnected power systems. Proceedings of the Second Carnegie Mellon Conference in Electric Power Systems: Monitoring, Sensing, Software and its Valuation for the Changing electric Power Industry.
Peer reviewed
The paper introduces a framework for information exchange and coordination of security assessment suitable for distributed multi-area control in large interconnections operated by a team of transmission system ...
Wehenkel, L., Glavic, M., Geurts, P., & Ernst, D. (2006). About automatic learning for advanced sensing, monitoring and control of electric power systems. Proceedings of the Second Carnegie Mellon Conference in Electric Power Systems: Monitoring, Sensing, Software and its Valuation for the Changing electric Power Industry.
Peer reviewed
The paper considers the possible uses of automatic learning for improving power system performance by software methodologies. Automatic learning per se is first reviewed and recent developements of the field ...
Wehenkel, L., Glavic, M., Geurts, P., & Ernst, D. (2006). Automatic learning of sequential decision strategies for dynamic security assessment and control. Proceedings of the IEEE Power Engineering Society General Meeting 2006.
Peer reviewed
This paper proposes to formulate security control as a sequential decision making problem and presents new developments in automatic learning of sequential decision making strategies from simulations and/or ...
Capitanescu, F., Glavic, M., & Wehenkel, L. (2005). Applications of an interior point method based optimal power flow. CEE 05 conference.
Peer reviewed
This paper tackles the complex problem of an Optimal Power Flow (OPF) by the Interior Point Method (IPM). Two interior point algorithms are presented and compared, namely the pure primal-dual and the ...
Capitanescu, F., Glavic, M., & Wehenkel, L. (2005). Experience with the multiple centrality corrections interior point algorithm for optimal power flow. CEE 05 conference.
Peer reviewed
This paper analyzes the ability of the Multiple Centrality Corrections (MCC) interior point algorithm to solve various classical optimal power flow (OPF) variants, namely: the minimization of generation ...
Capitanescu, F., Glavic, M., & Wehenkel, L. (2005, June). An interior point method based optimal power flow.
Peer reviewed
This paper deals with the solution of an optimal power flow (OPF) problem by the interior point method (IPM). The latter is a very appealing approach to this nonlinear programming problem due to its ...
Glavic, M., Ernst, D., & Wehenkel, L. (2005). A reinforcement learning based discrete supplementary control for power system transient stability enhancement. Engineering Intelligent Systems for Electrical Engineering and Communications, 13(2 Sp. Iss. SI), 81-88.
Peer reviewed
This paper proposes an application of a Reinforcement Learning (RL) method to the control of a dynamic brake aimed to enhance power system transient stability. The control law of the resistive brake is in the ...
Ernst, D., Geurts, P., & Wehenkel, L. (2005). Tree-based batch mode reinforcement learning. Journal of Machine Learning Research, 6, 503-556.
Peer reviewed (verified by ORBi)
Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. In batch mode, it can be achieved by approximating the so-called Q ...
Glavic, M., Ernst, D., & Wehenkel, L. (2005). Combining a stability and a performance-oriented control in power systems. IEEE Transactions on Power Systems, 20(1), 525-526.
Peer reviewed (verified by ORBi)
This paper suggests that the appropriate combination of a stability-oriented and a performance-oriented control technique is a promising way to implement advanced control schemes in power systems. The ...
Cirio, D., Lucarella, D., Vimercati, G., Massucco, S., Morini, A., Silvestro, F., Ernst, D., Pavella, M., & Wehenkel, L. (2005). Application of an advanced transient stability assessment and control method to a realistic power system. Proceedings of the 15th Power System Computation Conference (PSCC 2005).
Peer reviewed
The paper presents a technical overview of a large research project on Dynamic Security Assessment (DSA) supported by EU. Transient Stability Assessment and Control, which was one of the main goals of the ...
De Seny, D., Fillet, M., Meuwis, M.-A., Geurts, P., Lutteri, L., Ribbens, C., Bours, V., Wehenkel, L., Piette, J., Malaise, M., & Merville, M.-P. (2005). Discovery of new rheumatoid arthritis biomarkers using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry ProteinChip approach. Arthritis and Rheumatism, 52(12), 3801-12.
Peer reviewed (verified by ORBi)
OBJECTIVE: To identify serum protein biomarkers specific for rheumatoid arthritis (RA), using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology. METHODS: A ...
Druet, C., Vassena, S., Rousseaux, P., & Wehenkel, L. (2005). Application of a data minig based technique for the evaluation of transmission expansion plans. Proceedings of the 15th Power System Computation Conference (PSCC).
Peer reviewed
This paper describes a methodology for the study of long-term network planning under uncertainties. In this approach the major external uncertainties during the planning horizon are modelled as macro-scenarios ...
Ernst, D., Glavic, M., Geurts, P., & Wehenkel, L. (2005). Approximate value iteration in the reinforcement learning context. Application to electrical power system control. International Journal of Emerging Electrical Power Systems, 3(1).
Peer reviewed (verified by ORBi)
In this paper we explain how to design intelligent agents able to process the information acquired from interaction with a system to learn a good control policy and show how the methodology can be applied to ...
Geurts, P., Blanco Cuesta, A., & Wehenkel, L. (2005). Segment and combine approach for Biological Sequence Classification. Proc. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2005) (pp. 194--201).
Peer reviewed
This paper presents a new algorithm based on the segment and combine paradigm, for automatic classification of biological sequences. It classifies sequences by aggregating the information about their ...
Geurts, P., Fillet, M., De Seny, D., Meuwis, M.-A., Malaise, M., Merville, M.-P., & Wehenkel, L. (2005). Proteomic mass spectra classification using decision tree based ensemble methods. Bioinformatics, 21(14), 3138-45.
Peer reviewed (verified by ORBi)
MOTIVATION: Modern mass spectrometry allows the determination of proteomic fingerprints of body fluids like serum, saliva or urine. These measurements can be used in many medical applications in order to ...
Geurts, P., & Wehenkel, L. (2005). Closed-form dual perturb and combine for tree-based models. Proceedings of the International Conference on Machine Learning (ICML 2005).
Peer reviewed
This paper studies the aggregation of predictions made by tree-based models for several perturbed versions of the attribute vector of a test case. A closed-form approximation of this scheme combined with cross ...
Geurts, P., & Wehenkel, L. (2005). Segment and combine approach for non-parametric time-series classification. Lecture Notes in Computer Science, 3721, 478-485.
Peer reviewed
This paper presents a novel, generic, scalable, autonomous, and flexible supervised learning algorithm for the classification of multivariate and variable length time series. The essential ingredients of the ...
Marée, R., Geurts, P., Piater, J., & Wehenkel, L. (2005). Biomedical image classification with random subwindows and decision trees. Computer Vision for Biomedical Image Applications (pp. 220-229). Berlin: Springer-Verlag Berlin.
Peer reviewed
In this paper, we address a problem of biomedical image classification that involves the automatic classification of x-ray images in 57 predefined classes with large intra-class variability. To achieve that ...
Marée, R., Geurts, P., Piater, J., & Wehenkel, L. (2005). Decision Trees and Random Subwindows for Object Recognition. ICML workshop on Machine Learning Techniques for Processing Multimedia Content (MLMM2005).
Peer reviewed
In this paper, we compare five tree-based machine learning methods within a recent generic image classification framework based on random extraction and classification of subwindows. We evaluate them on three ...
Marée, R., Geurts, P., Piater, J., Wehenkel, L., Schmid, C. (Ed.), Soatto, S. (Ed.), & Tomasi, C. (Ed.). (2005). Random Subwindows for Robust Image Classification. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2005) (pp. 34--40).
Peer reviewed
We present a novel, generic image classification method based on a recent machine learning algorithm (ensembles of extremely randomized decision trees). Images are classified using randomly extracted ...
Wehenkel, L., Glavic, M., & Ernst, D. (2005). New developments in the application of automatic learning to power system control. Proceedings of the 15th Power System Computation Conference (PSCC 2005).
Peer reviewed
In this paper we present the basic principles of supervised learning and reinforcement learning as two complementary frameworks to design control laws or decision policies within the context of power system ...
Wehenkel, L., Ruiz-Vega, D., Ernst, D., & Pavella, M. (2005). Preventive and emergency control of power systems. Real Time Stability in Power Systems - Techniques for Early Detection of the Risk of Blackout (pp. 199-232). Springer Berlin / Heidelberg.
A general approach to real-time transient stability control is described, yielding various complementary techniques: pure preventive, open loop emergency, and closed loop emergency controls. The organization ...
Ernst, D., Glavic, M., & Wehenkel, L. (2004). Power systems stability control: Reinforcement learning framework. IEEE Transactions on Power Systems, 19(1), 427-435.
Peer reviewed (verified by ORBi)
In this paper, we explore how a computational approach to learning from interactions, called reinforcement learning (RL), can be applied to control power systems. We describe some challenges in power system ...
Auvray, V., Dobson, I., & Wehenkel, L. (2004). Modifying eigenvalue interactions near weak resonance. Proc. of International Symposium on Circuits and Systems (pp. 992 - V-995 Vol.5).
Peer reviewed
In electric power system instabilities such as subsynchronous resonance or interarea oscillations, two complex modes can approach each other in frequency and then interact by changing damping so that one of ...
Huang, J. A., Harrison, S., Vanier, G., Valette, A., & Wehenkel, L. (2004). Application of data mining to optimize settings for generator tripping and load shedding system in emergency control at Hydro-Quebec. COMPEL, 23(1 Sp. Iss. SI), 21-34.
Peer reviewed (verified by ORBi)
This paper describes the on-going work done by Hydro-Quebec to optimize the settings of automatic devices installed in its main power plants to maintain secure operation under extreme contingencies. The ...
Huang, J., Vanier, G., Valette, A., Harrison, S., Lévesque, F., & Wehenkel, L. (2004). Operation rules determined by risk analysis for special protection systems at Hydro-Québec.
Peer reviewed
This paper describes a new approach used by Hydro-Québec to determine the rules of automatic devices installed in its main power plants to maintain secure operation under extreme contingencies. An example of ...
Marée, R., Geurts, P., Piater, J., Wehenkel, L., Hong, K.-S. (Ed.), & Zhang, Z. (Ed.). (2004). A generic approach for image classification based on decision tree ensembles and local sub-windows. Proceedings of the 6th Asian Conference on Computer Vision (pp. 860-865). Asian Federation of Computer Vision Societies (AFCV).
Peer reviewed
A novel and generic approach for image classification is presented. The method operates directly on pixel values and does not require feature extraction. It combines a simple local sub-window extraction ...
Olaru, C., & Wehenkel, L. (2004). Bias-variance tradeoff of soft decision trees.
Peer reviewed
This paper focuses on the study of the error composition of a fuzzy decision tree induction method recently proposed by the authors, called soft decision trees. This error may be expressed as a ...
Wehenkel, L., & Pavella, M. (2004). Preventive vs. emergency control of power systems.
Peer reviewed
A general approach to real-time transient stability control is described, yielding various complementary techniques: pure preventive, open loop emergency, and closed loop emergency controls. The ...
Olaru, C., & Wehenkel, L. (2003). A complete fuzzy decision tree technique. Fuzzy Sets and Systems, 138(2), 221-254.
Peer reviewed (verified by ORBi)
In this paper, a new method of fuzzy decision trees called soft decision trees (SDT) is presented. This method combines tree growing and pruning, to determine the structure of the soft decision tree, with ...
Bihain, A., Cirio, D., Fiorina, M., Lopez, R., Lucarella, D., Massucco, S., Ruiz Vega, D., Vournas, C., Van Cutsem, T., & Wehenkel, L. (2003). OMASES: A DYNAMIC SECURITY ASSESSMENT TOOL FOR THE NEW MARKET ENVIRONMENT. Proc. IEEE Bologna Power Tech Conference (pp. 473).
Peer reviewed
The paper presents the efforts and results of a large consortium of European Industries, Research Centers and Universities involved in an EU research project named OMASES in the field of Power System Dynamic ...
Del Angel, A., Glavic, M., & Wehenkel, L. (2003). Using artificial neural networks to estimate rotor angles and speeds from phasor measurements.
Peer reviewed
This paper deals with an improved use of phasor measurements. In particular, the paper focuses on the development of a technique for estimation of generator rotor angle and speed, based on phasor ...
Ernst, D., Geurts, P., & Wehenkel, L. (2003). Iteratively extending time horizon reinforcement learning. Machine Learning: ECML 2003, 14th European Conference on Machine Learning (pp. 96-107). Berlin: Springer-Verlag Berlin.
Peer reviewed
Reinforcement learning aims to determine an (infinite time horizon) optimal control policy from interaction with a system. It can be solved by approximating the so-called Q-function from a sample of four ...
Glavic, M., Ernst, D., & Wehenkel, L. (2003). A reinforcement learning based discrete supplementary control for power system transient stability enhancement. Proceedings of the 12th Intelligent Systems Application to Power Systems Conference (ISAP 2003).
Peer reviewed
This paper proposes an application of a Reinforcement Learning (RL) method to the control of a dynamic brake aimed to enhance power system transient stability. The control law of the resistive brake is in the ...
Marée, R., Geurts, P., & Wehenkel, L. (2003). Une méthode générique pour la classification automatique d'images à partir des pixels. Revue des Nouvelles Technologies de l'Information, 1, 227-238.
Peer reviewed
Dans cet article, nous évaluons une approche générique de classification automatique d'images. Elle repose sur une méthode d'apprentissage récente qui construit des ensembles d'arbres de décision par sélection ...
N'Guessan, A., Pavella, M., & Wehenkel, L. (2003). An implementation of on-line transient stability screening and control using distributed processing. Proc. of Intelligent Systems Application to Power Systems (pp. 6).
Peer reviewed
This paper describes the implementation of an online transient stability assessment software, composed of algorithms for contingency screening and for the design of preventive control actions. The ...
Vassena, S., Mack, P., Druet, C., Rousseaux, P., & Wehenkel, L. (2003). A probabilistic approach to power system network planning under uncertainties. Proceedings of the IEEE Bologna Power Tech Conference (pp. 6 pp. Vol.2).
Peer reviewed
This work proposes a methodology and a practical tool for the study of long-term network planning under uncertainties. In this approach the major external uncertainties during the planning horizon are modeled ...
Auvray, V., & Wehenkel, L. (2002). On the construction of the inclusion boundary neighbourhood for markov equivalence classes of bayesian network structures. Proceedings of Uncertainty in Artificial Intelligence (pp. 10).
Peer reviewed
The problem of learning Markov equivalence classes of Bayesian network structures may be solved by searching for the maximum of a scoring metric in a space of these classes. This paper deals with the ...
Ernst, D., & Wehenkel, L. (2002). FACTS devices controlled by means of reinforcement learning algorithms. Proceedings of the 14th Power Systems Computation Conference (PSCC 2002).
Peer reviewed
Reinforcement learning consists of a collection of methods for approximating solutions to deterministic and stochastic optimal control problems of unknown dynamics. These methods learn by experience how to ...
Geurts, P., Olaru, C., & Wehenkel, L. (2001). Improving the bias/variance tradeoff of decision trees - towards soft tree induction. Engineering intelligent systems, 9, 195-204.
Peer reviewed
One of the main difficulties with standard top down induction of decision trees comes from the high variance of these methods. High variance means that, for a given problem and sample size, the resulting tree ...
Druet, C., Ernst, D., & Wehenkel, L. (2000). Application of reinforcement learning to electrical power system closed-loop emergency control. Principles of Data Mining and Knowledge Discovery, 4th European Conference, PKDD 2000. Springer Berlin / Heidelberg.
Peer reviewed
This paper investigates the use of reinforcement learning in electric power system emergency control. The approach consists of using numerical simulations together with on-policy Monte Carlo control to ...
Geurts, P., & Wehenkel, L. (2000). Investigation and reduction of discretization Variance in decision tree induction. Proceedings of ECML 2000, European Conference on Machine Learning (pp. 162-170). Springer-Verlag.
Peer reviewed
This paper focuses on the variance introduced by the discretization techniques used to handle continuous attributes in decision tree induction. Different discretization procedures are first studied empirically ...
Geurts, P., & Wehenkel, L. (2000). Temporal machine learning for switching control. Proceedings of PKDD 2000, 4th European Conference on Principles of Data Mining and Knowledge Discovery (pp. 401-408). Lyon, France: Springer-Verlag.
Peer reviewed
In this paper, a temporal machine learning method is presented which is able to automatically construct rules allowing to detect as soon as possible an event using past and present measurements made on a ...
Bettiol, A., Ruiz-Vega, D., Ernst, D., Wehenkel, L., & Pavella, M. (1999). Transient stability-constrained optimal power flow. Proceedings of the IEEE Power Tech'99.
Peer reviewed
This paper proposes a new approach able to maximize the interface flow limits in power systems and to find a new operating state that is secure with respect to both, dynamic (transient stability) and static ...
Olaru, C., Geurts, P., & Wehenkel, L. (1999). Data mining tools and application in power system engineering. Proceedings of the 13th Power System Computation Conference, PSCC99 (pp. 324-330). Trondheim, Norway.
Peer reviewed
The power system field is presently facing an explosive growth of data. The data mining (DM) approach provides tools for making explicit some implicit subtle structure in data. Applying data mining to power ...
Wehenkel, L. (1999). Emergency control and its strategies. Proceedings of the Power Systems Computation Conference 1999 (pp. 14).
Peer reviewed
The objective of this paper is to discuss research trends in the context of power system emergency control. First, different possible strategies are discussed for the design of emergency control schemes ...
Wehenkel, L., Lebrevelec, C., Trotignon, M., & Batut, J. (1999). Probabilistic design of power-system special stability controls. Control Engineering Practice, 7(2), 183-194.
Peer reviewed (verified by ORBi)
A probabilistic approach to the design of power-system special stability controls is presented here. Using Monte-Carlo simulations, it takes into account all the potential causes of blackouts, slow and fast ...
SANCHEZ-UBEDA, E., & Wehenkel, L. (1998). The Hinges model: A one-dimensional continuous piecewise polynomial model. Proc of IPMU'98 (pp. 878-885).
Peer reviewed
In this article we propose an efficient approach to flexible and robust one-dimensional curve fitting under stringent high noise conditions. This is an important subproblem arising in many automatic ...
Ernst, D., Bettiol, A., Zhang, Y., Wehenkel, L., & Pavella, M. (1998). Real-time transient stability emergency control of the South-Southeast Brazilian system. Proceedings of SEPOPE 1998.
A method is proposed for during transients emergency control which predicts the evolution of a system undergoing a major disturbance and, if loss of synchronism is anticipated decides control actions able to ...
Geurts, P., & Wehenkel, L. (1998). Early prediction of electric power system blackouts by temporal machine learning. Proceedings of ICML-AAAI 98 Workshop on "Predicting the future: AI approaches to time series analysis" (pp. 21-27). Madison (Wisconsin).
Peer reviewed
This paper discusses the application of machine learning to the design of power system blackout prediction criteria, using a large database of random power system scenarios generated by Monte-Carlo simulation ...
Wehenkel, L. (1997). Machine-learning approaches to power-system security assessment. IEEE Expert, 12(5), 60-72.
Peer reviewed
The paper discusses a framework that uses machine learning and other automatic-learning methods to assess power-system security. The framework exploits simulation models in parallel to screen diverse ...
Zhang, Y., Wehenkel, L., Rousseaux, P., & Pavella, M. (1997). SIME: A hybrid approach to fast transient stability assessment and contingency selection. International Journal of Electrical Power & Energy Systems, 19(3), 195-208.
Peer reviewed (verified by ORBi)
We propose an integrated scheme for transient stability assessment which in a sequence screens contingencies and scrutinizes only the selected ones. This scheme is based on a hybrid method, called SIME for ...
Wehenkel, L. (1996). Contingency severity assessment for voltage security using non-parametric regression techniques. IEEE Transactions on Power Systems, 11(1), 101-111.
Peer reviewed (verified by ORBi)
This paper proposes a novel approach to power system voltage security assessment exploiting nonparametric regression techniques to extract simple, and at the same time reliable, models of the severity of a ...
Wehenkel, L. (1996). On uncertainty measures used for decision tree induction. In B., Bouchon-Meunier (Ed.), Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 6).
Peer reviewed
This paper provides a further look at uncertainty or information criteria used in the context of deci- sion tree induction, and more generally of learn- ing conditional class probability models. We show the ...
Wehenkel, L. (1993). Decision tree pruning using an additive information quality measure. In B., Bouchon-Meunier, L., Valverde, & R., Yager (Eds.), Uncertainty in Intelligent Systems (pp. 397-411). Elsevier-North Holland.
An additive quality measure based on information theory is introduced for the inductive inference of decision trees. It takes into account both the information content and the complexity of a tree, combined so ...
Wehenkel, L., & Pavella, M. (1993). Decision tree approach to power systems security assessment. International Journal of Electric Power and Energy Systems, 15(1), 13-36.
Peer reviewed (verified by ORBi)
An overview of the general decision tree approach to power system security assessment is presented. The general decision tree methodology is outlined, modifications proposed in the context of transient ...
Wehenkel, L., & Pavella, M. (1991). Decision Trees and Transient Stability of Electric Power Systems. Automatica, 27(1), 115-134.
Peer reviewed (verified by ORBi)
An inductive inference method for the automatic building of decision trees is investigated. Among its various tasks, the splitting and the stop splitting criteria successively applied to the nodes of a grown ...
Wehenkel, L., Van Cutsem, T., & Pavella, M. (1989). An Artificial Intelligence Framework for On-Line Transient Stability Assessment of Power Systems. IEEE Transactions on Power Systems, 4(2), 789-800.
Peer reviewed (verified by ORBi)
A framework is proposed to tackle the online transient stability problem of power systems. Based on artificial intelligence, it successively makes use of an inductive inference method to build decisions ...