Risk Estimation and Feature Selection: Proceedings of European Symposium on Artificial Neural Networks (ESANN 2013)

Gauthier Doquire, Benoît Frénay, Michel Verleysen

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Résumé

For classification problems, the risk is often the criterion to be eventually minimised. It can thus naturally be used to assess the quality of feature subsets in feature selection. However, in practice, the probability of error is often unknown and must be estimated. Also, mutual information is often used as a criterion to assess the quality of feature subsets, since it can be seen as an imperfect proxy for the risk and can be reliably estimated. In this paper, two different ways to estimate the risk using the Kozachenko-Leonenko probability density estimator are proposed. The resulting estimators are compared on feature selection problems with a mutual information estimator based on the same density estimator. Along the line of our previous works, experiments show that using an estimator of either the risk or the mutual information give similar results.
langue originaleAnglais
titreESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages161-166
Nombre de pages6
Etat de la publicationPublié - 2013
Modification externeOui
Evénement21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 - Bruges, Belgique
Durée: 24 avr. 201326 avr. 2013

Une conférence

Une conférence21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013
PaysBelgique
La villeBruges
période24/04/1326/04/13

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    Doquire, G., Frénay, B., & Verleysen, M. (2013). Risk Estimation and Feature Selection: Proceedings of European Symposium on Artificial Neural Networks (ESANN 2013). Dans ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (p. 161-166)