Feature Selection Filters Based on the Permutation Test

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.