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SPSA-FSR: Simultaneous Perturbation Stochastic Approximation for Feature Selection and Ranking
After a review of the current state-of-the-art, we discuss our improvements in detail and present three sets of computational experiments: (1) comparison of SPSA-FS as a (wrapper) feature selection method against sequential methods as well as genetic algorithms, (2) comparison of SPSA-FS as a feature ranking method in a classification setting against random forest importance, chi-squared, and information main methods, and (3) comparison of SPSA-FS as a feature ranking method in a regression setting against minimum redundancy maximum relevance (MRMR), RELIEF, and linear correlation methods.
Feature Selection via Binary Simultaneous Perturbation Stochastic Approximation
Feature selection (FS) has become an indispensable task in dealing with today's highly complex pattern recognition problems with massive number of features.
