Social Impact based Approach to Feature Subset Selection

The interactions taking place in the society could be a source of rich inspiration for the development of novel computational methods. This paper describes an application of two optimization methods based on the idea of social interactions. The first one is the Social Impact Theory based Optimizer - a novel method directly inspired by and based on the Dynamic Theory of Social Impact known from social psychology. The second one is the binary Particle Swarm Optimization - well known optimization technique, which could be understood as to be inspired by decision making process in a group. The two binary optimization methods are applied in the area of automatic pattern classification to selection of an optimal subset of classifier's inputs. The testing is performed using four datasets from UCI repository. The results show the ability of both methods to significantly reduce input dimensionality and simultaneously keep up the generalization ability.