### Abstract

We investigate the problem of supervised feature selection within the filtering framework. In our approach, applicable to the two-class problems, the feature strength is inversely proportional to the p-value of the null hypothesis that its class-conditional densities, p(X|Y = 0) and p(X|Y = 1), are identical. To estimate the p-values, we use Fisher's permutation test combined with the four simple filtering criteria in the roles of test statistics: sample mean difference, symmetric Kullback-Leibler distance, information gain, and chi-square statistic. The experimental results of our study, performed using naive Bayes classifier and support vector machines, strongly indicate that the permutation test improves the above-mentioned filters and can be used effectively when sample size is relatively small and number of features relatively large.

Original language | English |
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Title of host publication | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |

Editors | J.-F. Boulicaut, F. Esposito, D. Pedreschi, F. Giannotti |

Pages | 334-346 |

Number of pages | 13 |

Volume | 3201 |

State | Published - 2004 |

Event | 15th European Conference on Machine Learning, ECML 2004 - Pisa, Italy Duration: Sep 20 2004 → Sep 24 2004 |

### Other

Other | 15th European Conference on Machine Learning, ECML 2004 |
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Country | Italy |

City | Pisa |

Period | 9/20/04 → 9/24/04 |

### ASJC Scopus subject areas

- Hardware and Architecture
- Computer Science(all)
- Theoretical Computer Science

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## Cite this

*Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)*(Vol. 3201, pp. 334-346)