References of "Wehenkel, Louis"
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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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See detailPreventive vs. emergency control of power systems
Wehenkel, Louis ULg; Pavella, Mania ULg

(2004)

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 detailWhither dynamic congestion management ?
Wehenkel, Louis ULg

(2004)

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See detailOperation rules determined by risk analysis for special protection systems at Hydro-Québec
Huang, Jinan; Vanier, Guy; Valette, Alain et al

(2004)

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 ... [more ▼]

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 application of this approach is given to illustrate how to apply data mining technique for the rules of the automatic generator rejection and remote load shedding system (RPTC: Rejet de Production et Télédélestage de Charges in French) installed at the Churchill Falls hydroelectric power plant (5500 MW) in Labrador. Real time snapshots of the Hydro-Québec power system collected over several years data have been used to generate large amounts of results (database) by transient stability simulations. The database is processed by the data mining software developed by the University of Liege to construct the decision trees. This approach gives the most relevant parameters and finds optimal settings for the RPTC system at the Churchill Falls, minimizing the number of generator rejection while maintaining the same performance in terms of security coverage. New operation rules have thus been established. [less ▲]

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See detailBias-variance tradeoff of soft decision trees
Olaru, Cristina; Wehenkel, Louis ULg

(2004)

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 sum of ... [more ▼]

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 sum of three types of error: residual error, bias and variance. The paper studies empirically the tradeoff between bias and variance in a soft decision tree method and compares it with the tradeoff of classical crisp regression and classification trees. The main conclusion is that the reduced prediction variance of fuzzy trees is the main reason for their improved performance with respect to crisp ones. [less ▲]

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See detailErratum to : A complete fuzzy decision tree technique (vol 138, pg 221, 2003)
Olaru, C.; Wehenkel, Louis ULg

in Fuzzy Sets and Systems (2003), 140(3), 563-565

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See detailA complete fuzzy decision tree technique
Olaru, C.; Wehenkel, Louis ULg

in Fuzzy Sets and Systems (2003), 138(2), 221-254

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 ... [more ▼]

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 refitting and backfitting, to improve its generalization capabilities. The method is explained and motivated and its behavior is first analyzed empirically on 3 large databases in terms of classification error rate, model complexity and CPU time. A comparative study on 11 standard UCI Repository databases then shows that the soft decision trees produced by this method are significantly more accurate than standard decision trees. Moreover, a global model variance study shows a much lower variance for soft decision trees than for standard trees as a direct cause of the improved accuracy. (C) 2003 Elsevier B.V. All rights reserved. [less ▲]

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See detailOMASES: A DYNAMIC SECURITY ASSESSMENT TOOL FOR THE NEW MARKET ENVIRONMENT
Bihain, André; Cirio, Diego; Fiorina, Mario et al

(2003, June 26)

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 ... [more ▼]

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 Security Assessment (DSA). The overall structure of an on-line DSA tool including TSA – Transient Stability Assessment, VSA – Voltage Stability Assessment, TS – Training Simulator and MS – Market Simulator is reported. Some basic assumptions and methodological aspects of the tool are presented considering its possible use within actual or future Energy Management Systems under the new electric market environment. Scenarios set up for the validation phase and results are reported with reference to the Italian test facility. [less ▲]

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See detailA probabilistic approach to power system network planning under uncertainties
Vassena, Stefano; Mack, Philippe; Druet, Christophe et al

in Proceedings of the IEEE Bologna Power Tech Conference (2003)

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 ... [more ▼]

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 as macroscenarios at different future time instants. On the other hand, the random nature of actual operating conditions is taken into account by using a probabilistic model of microscenarios based on past statistics. Massive Monte-Carlo simulations are used to generate and simulate a large number of scenarios and store the detailed results in a relational database. Data mining techniques are then applied to extract information from the database so as to rank scenarios and network reinforcements according to different criteria. [less ▲]

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