Simplified social impact theory based optimizer in feature subset selection

Abstract

This chapter proposes a simplification of the original Social Impact Theory based Optimizer (oSITO). Based on the experiments with seven benchmark datasets it is shown that the novel method called simplified Social Impact Theory based Optimizer (sSITO) does not degrade the optimization abilities and even leads to smaller testing error and better dimensionality reduction. From these points of view, it also outperforms another well known social optimizer - the binary Particle Swarm Optimization algorithm. The main advantages of the method are the simple implementation and the small number of parameters (two). Additionally, it is empirically shown that the sSITO method even outperforms the nearest neighbor margin based SIMBA algorithm. © 2011 Springer-Verlag Berlin Heidelberg.