SPSA-FSR: Simultaneous Perturbation Stochastic Approximation for Feature Selection and Ranking

5 code implementations • 16 Apr 2018 • Zeren D. Yenice, Niranjan Adhikari, Yong Kai Wong, Vural Aksakalli, Alev Taskin Gumus, Babak Abbasi

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 General Classification +1