Non-parametric feature selection for multiple class processes

Abstract

A method of feature selection is presented that has linear computational complexity, and ways are shown how to use it when the type of the probability density function is unknown. There is no claim that the procedures for nonparametric probability density function estimation are applicable to any thinkable distribution, but the lower bound for the classifier performance estimation makes the presented measure applicable in most practical cases. Simulations with synthetic test data as well as references to applications with real-world data demonstrate the applicability of the measure discussed.