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. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Radivojac, P., Obradovic, Z., Keith Dunker, A., & Vucetic, S. (2004). Feature selection filters based on the permutation test. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3201, pp. 334–346). Springer Verlag. https://doi.org/10.1007/978-3-540-30115-8_32
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