References of "Wehenkel, Louis"
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See detailTree-based batch mode reinforcement learning
Ernst, Damien ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

in Journal of Machine Learning Research (2005), 6

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 ... [more ▼]

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-function based on a set of four-tuples (x(t), u(t), r(t), x(t+1)) where x(t) denotes the system state at time t, u(t) the control action taken, r(t) the instantaneous reward obtained and x(t+1) the successor state of the system, and by determining the control policy from this Q-function. The Q-function approximation may be obtained from the limit of a sequence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) and two newly proposed ensemble algorithms, namely extremely and totally randomized trees. We study their performances on several examples and find that the ensemble methods based on regression trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accuracy results are provided by the extremely randomized trees. [less ▲]

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See detailCombining a stability and a performance-oriented control in power systems
Glavic, M.; Ernst, Damien ULg; Wehenkel, Louis ULg

in IEEE Transactions on Power Systems (2005), 20(1), 525-526

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 ... [more ▼]

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 particular approach considered combines control Lyapunov functions (CLF) and reinforcement learning. The capabilities of the resulting controller are illustrated on a control problem involving a thyristor-controlled series capacitor (TCSC) device for damping oscillations in a four-machine power system. [less ▲]

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See detailApplication of a data minig based technique for the evaluation of transmission expansion plans
Druet, Christophe; Vassena, Stefano; Rousseaux, Patricia ULg et al

in Proceedings of the 15th Power System Computation Conference (PSCC) (2005)

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 ... [more ▼]

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 at different future time instants. The random nature of actual operating conditions is taken into account by using a probabilistic model of micro-scenarios based on past statistics. MonteCarlo simulations are used to generate and simulate a specified number of scenarios. Data mining techniques are then applied to the simulations results collected in a database, so as to extract information and to rank scenarios and network reinforcements according to different performance criteria. The paper describes the application of this approach on a real transmission planning problem faced by the Belgian transmission system operator. [less ▲]

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See detailPreventive and emergency control of power systems
Wehenkel, Louis ULg; Ruiz-Vega, Daniel; Ernst, Damien ULg et al

in Real Time Stability in Power Systems - Techniques for Early Detection of the Risk of Blackout (2005)

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 ... [more ▼]

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 of the resulting control schemes is then revisited in order to make it able to cover static and voltage security, in addition to transient stability. Distinct approaches for preventive and emergency operating conditions are advocated. [less ▲]

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See detailApproximate value iteration in the reinforcement learning context. Application to electrical power system control
Ernst, Damien ULg; Glavic, Mevludin; Geurts, Pierre ULg et al

in International Journal of Emerging Electrical Power Systems (2005), 3(1),

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 ... [more ▼]

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 control some devices aimed to damp electrical power oscillations. The control problem is formalized as a discrete-time optimal control problem and the information acquired from interaction with the system is a set of samples, where each sample is composed of four elements: a state, the action taken while being in this state, the instantaneous reward observed and the successor state of the system. To process this information we consider reinforcement learning algorithms that determine an approximation of the so-called Q-function by mimicking the behavior of the value iteration algorithm. Simulations are first carried on a benchmark power system modeled with two state variables. Then we present a more complex case study on a four-machine power system where the reinforcement learning algorithm controls a Thyristor Controlled Series Capacitor (TCSC) aimed to damp power system oscillations. [less ▲]

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See detailNew developments in the application of automatic learning to power system control
Wehenkel, Louis ULg; Glavic, Mevludin; Ernst, Damien ULg

in Proceedings of the 15th Power System Computation Conference (PSCC 2005) (2005)

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 ... [more ▼]

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 control. We also review recent developments in the realm of automatic learning methods and discuss their applicability to power system decision and control problems. Simulation results illustrating the potentials of the recently introduced fitted Q iteration learning algorithm in controlling a TCSC device aimed to damp electro-mechanical oscillations in a synthetic 4-machine system, are included in the paper. [less ▲]

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See detailApplication of an advanced transient stability assessment and control method to a realistic power system
Cirio, D.; Lucarella, D.; Vimercati, G. et al

in Proceedings of the 15th Power System Computation Conference (PSCC 2005) (2005)

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 ... [more ▼]

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 project, is taken into consideration by presenting the fundamental theoretical methodology and possible applications. A specific prototype installation for a realistic power system is then reported by presenting and commenting some of the obtained results. [less ▲]

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See detailRandom Subwindows for Robust Image Classification
Marée, Raphaël ULg; Geurts, Pierre ULg; Piater, Justus ULg et al

in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2005) (2005)

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 ... [more ▼]

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 subwindows that are suitably normalized to yield robustness to certain image transformations. Our method is evaluated on four very different, publicly available datasets (COIL-100, ZuBuD, ETH-80, WANG). Our results show that our automatic approach is generic and robust to illumination, scale, and viewpoint changes. An extension of the method is proposed to improve its robustness with respect to rotation changes. [less ▲]

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See detailDecision Trees and Random Subwindows for Object Recognition
Marée, Raphaël ULg; Geurts, Pierre ULg; Piater, Justus ULg et al

in ICML workshop on Machine Learning Techniques for Processing Multimedia Content (MLMM2005) (2005)

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 ... [more ▼]

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 publicly available object recognition datasets (COIL-100, ETH-80, and ZuBuD). Our comparison shows that this general and conceptually simple framework yields good results when combined with ensemble of decision trees, especially when using Tree Boosting or Extra-Trees. The latter is also particularly attractive in terms of computational efficiency. [less ▲]

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See detailSegment and combine approach for Biological Sequence Classification
Geurts, Pierre ULg; Blanco Cuesta, Antia; Wehenkel, Louis ULg

in Proc. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2005) (2005)

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 ... [more ▼]

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 subsequences predicted by a classifier derived by machine learning from a random sample of training subsequences. This generic approach is combined with decision tree based ensemble methods, scalable both with respect to sample size and vocabulary size. The method is applied to three families of problems: DNA sequence recognition, splice junction detection, and gene regulon prediction. With respect to standard approaches based on n-grams, it appears competitive in terms of accuracy, flexibility, and scalability. The paper also highlights the possibility to exploit the resulting models to identify interpretable patterns specific of a given class of biological sequences. [less ▲]

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See detailProteomic mass spectra classification using decision tree based ensemble methods.
Geurts, Pierre ULg; Fillet, Marianne ULg; De Seny, Dominique ULg et al

in Bioinformatics (2005), 21(14), 3138-45

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 ... [more ▼]

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 diagnose the current state or predict the evolution of a disease. Recent developments in machine learning allow one to exploit such datasets, characterized by small numbers of very high-dimensional samples. RESULTS: We propose a systematic approach based on decision tree ensemble methods, which is used to automatically determine proteomic biomarkers and predictive models. The approach is validated on two datasets of surface-enhanced laser desorption/ionization time of flight measurements, for the diagnosis of rheumatoid arthritis and inflammatory bowel diseases. The results suggest that the methodology can handle a broad class of similar problems. [less ▲]

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See detailSegment and combine approach for non-parametric time-series classification
Geurts, Pierre ULg; Wehenkel, Louis ULg

in Lecture Notes in Computer Science (2005), 3721

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 ... [more ▼]

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 algorithm are randomization, segmentation of time-series, decision tree ensemble based learning of subseries classifiers, combination of subseries classification by voting, and cross-validation based temporal resolution adaptation. Experiments are carried out with this method on 10 synthetic and real-world datasets. They highlight the good behavior of the algorithm on a large diversity of problems. Our results are also highly competitive with existing approaches from the literature. [less ▲]

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See detailBiomedical image classification with random subwindows and decision trees
Marée, Raphaël ULg; Geurts, Pierre ULg; Piater, Justus ULg et al

in Computer Vision for Biomedical Image Applications (2005)

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 ... [more ▼]

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 goal, we apply and slightly adapt a recent generic method for image classification based on ensemble of decision trees and random subwindows. We obtain classification results close to the state of the art on a publicly available database of 10000 x-ray images. We also provide some clues to interpret the classification of each image in terms of subwindow relevance. [less ▲]

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See detailDiscovery of new rheumatoid arthritis biomarkers using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry ProteinChip approach.
De Seny, Dominique ULg; Fillet, Marianne ULg; Meuwis, Marie-Alice ULg et al

in Arthritis and Rheumatism (2005), 52(12), 3801-12

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 ... [more ▼]

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 total of 103 serum samples from patients and healthy controls were analyzed. Thirty-four of the patients had a diagnosis of RA, based on the American College of Rheumatology criteria. The inflammation control group comprised 20 patients with psoriatic arthritis (PsA), 9 with asthma, and 10 with Crohn's disease. The noninflammation control group comprised 14 patients with knee osteoarthritis and 16 healthy control subjects. Serum protein profiles were obtained by SELDI-TOF-MS and compared in order to identify new biomarkers specific for RA. Data were analyzed by a machine learning algorithm called decision tree boosting, according to different preprocessing steps. RESULTS: The most discriminative mass/charge (m/z) values serving as potential biomarkers for RA were identified on arrays for both patients with RA versus controls and patients with RA versus patients with PsA. From among several candidates, the following peaks were highlighted: m/z values of 2,924 (RA versus controls on H4 arrays), 10,832 and 11,632 (RA versus controls on CM10 arrays), 4,824 (RA versus PsA on H4 arrays), and 4,666 (RA versus PsA on CM10 arrays). Positive results of proteomic analysis were associated with positive results of the anti-cyclic citrullinated peptide test. Our observations suggested that the 10,832 peak could represent myeloid-related protein 8. CONCLUSION: SELDI-TOF-MS technology allows rapid analysis of many serum samples, and use of decision tree boosting analysis as the main statistical method allowed us to propose a pattern of protein peaks specific for RA. [less ▲]

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See detailClosed-form dual perturb and combine for tree-based models
Geurts, Pierre ULg; Wehenkel, Louis ULg

in Proceedings of the International Conference on Machine Learning (ICML 2005) (2005)

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 ... [more ▼]

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-validation to tune the level of perturbation is proposed. This yields soft-tree models in a parameter free way, and reserves their interpretability. Empirical evaluations, on classification and regression problems, show that accuracy and bias/variance tradeoff are improved significantly at the price of an acceptable computational overhead. The method is further compared and combined with tree bagging. [less ▲]

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See detailDiscovery of new rheumatoid arthritis biomarkers using SELDI-TOF-MS ProteinChip approach
de Seny, D. M.; Fillet, Marianne ULg; Meuwis, Marie-Alice ULg et al

in Arthritis and Rheumatism (2004, September), 50(9, Suppl. S), 124

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See detailPower systems stability control: Reinforcement learning framework
Ernst, Damien ULg; Glavic, Mevludin; Wehenkel, Louis ULg

in IEEE Transactions on Power Systems (2004), 19(1), 427-435

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 ... [more ▼]

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 control and discuss how some of those challenges could be met by using these RL methods. The difficulties associated with their application to control power systems are described and discussed as well as strategies that can be adopted to overcome them. Two reinforcement learning modes are considered: the online mode in which the interaction occurs with the real power system and the offline mode in which the interaction occurs with a simulation model of the real power system. We present two case studies made on a four-machine power system model. The first one concerns the design by means of RL algorithms used in offline mode of a dynamic brake controller. The second concerns RL methods used in online mode when applied to control a thyristor controlled series capacitor (TCSC) aimed to damp power system oscillations. [less ▲]

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See detailA generic approach for image classification based on decision tree ensembles and local sub-windows
Marée, Raphaël ULg; Geurts, Pierre ULg; Piater, Justus ULg et al

in Proceedings of the 6th Asian Conference on Computer Vision (2004)

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 ... [more ▼]

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 technique with induction of ensembles of extremely randomized decision trees. We report results on four well known and publicly available datasets corresponding to representative applications of image classification problems: handwritten digits (MNIST), faces (ORL), 3D objects (COIL-100), and textures (OUTEX). A comparison with studies from the computer vision literature shows that our method is competitive with the state of the art, an interesting result considering its generality and conceptual simplicity. Further experiments are carried out on the COIL-100 dataset to evaluate the robustness of the learned models to rotation, scaling, or occlusion of test images. These preliminary results are very encouraging [less ▲]

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See detailApplication of data mining to optimize settings for generator tripping and load shedding system in emergency control at Hydro-Quebec
Huang, J. A.; Harrison, S.; Vanier, G. et al

in COMPEL (2004), 23(1 Sp. Iss. SI), 21-34

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 ... [more ▼]

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 automatic generator tripping and load shedding system (RPTC) described in this paper is installed at the Churchill Falls hydroelectric power plant (5,500 MW) in Labrador. Data mining techniques such as decision trees and regression trees have been used. Real time snapshots of the Hydro-Quebec power system collected over a 5 year period have been used to generate large amounts of results by transient stability simulations. The processing of these data has been done using software developed by the University of Liege. This approach gives the most relevant parameters and finds optimal settings for the RPTC system, minimizing the number of tripped generator units while maintaining the same performance in terms of security coverage. New operation rules can thus be established. [less ▲]

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See detailModifying eigenvalue interactions near weak resonance
Auvray, Vincent; Dobson, Ian; Wehenkel, Louis ULg

in Proc. of International Symposium on Circuits and Systems (2004)

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 ... [more ▼]

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 the modes becomes unstable. Selecting changes in parameters to minimize this interaction is difficult by trial and error. By analyzing the interaction as a perturbation of a weak resonance, we calculate sensitivities that indicate the parameters to be changed to minimize the interaction and stabilize the system. The method is illustrated with a simple example of two coupled linear oscillators. The use of sensitivity methods to change the type of the interaction is also demonstrated. [less ▲]

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