Feature Selection for High-Dimensional Data: A Kolmogorov-Smirnov Correlation-Based Filter

Abstract

An algorithm for filtering information based on the Kolmogorov-Smirnov correlation-based approach has been implemented and tested on feature selection. The only parameter of this algorithm is statistical confidence level that two distributions are identical. Empirical comparisons with 4 other state-of-the-art features selection algorithms (FCBP, CorrSF, ReliefF and ConnSF) are very encouraging.