### 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 originale | Anglais |
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titre | ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |

Pages | 161-166 |

Nombre de pages | 6 |

Etat de la publication | Publié - 2013 |

Modification externe | Oui |

Evénement | 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 - Bruges, Belgique Durée: 24 avr. 2013 → 26 avr. 2013 |

### Une conférence

Une conférence | 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 |
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Pays | Belgique |

La ville | Bruges |

période | 24/04/13 → 26/04/13 |

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## Contient cette citation

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)