Feature selection filters based on the permutation test

Predrag Radivojac, Zoran Obradovic, A. Dunker, Slobodan Vucetic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations

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 languageEnglish
Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
EditorsJ.-F. Boulicaut, F. Esposito, D. Pedreschi, F. Giannotti
Pages334-346
Number of pages13
Volume3201
StatePublished - 2004
Event15th European Conference on Machine Learning, ECML 2004 - Pisa, Italy
Duration: Sep 20 2004Sep 24 2004

Other

Other15th European Conference on Machine Learning, ECML 2004
CountryItaly
CityPisa
Period9/20/049/24/04

ASJC Scopus subject areas

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

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  • Cite this

    Radivojac, P., Obradovic, Z., Dunker, A., & Vucetic, S. (2004). Feature selection filters based on the permutation test. In J-F. Boulicaut, F. Esposito, D. Pedreschi, & F. Giannotti (Eds.), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3201, pp. 334-346)