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

Gauthier Doquire, Benoît Frénay, Michel Verleysen

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Pages161-166
Number of pages6
Publication statusPublished - 2013
Externally publishedYes
Event21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 - Bruges, Belgium
Duration: 24 Apr 201326 Apr 2013

Conference

Conference21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013
CountryBelgium
CityBruges
Period24/04/1326/04/13

Keywords

  • Risk estimation ICTEAM:MLAI

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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). In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 161-166)