Fuzzy Rough Set-Based Feature Selection with Improved Seed Population in PSO and IDS

Abstract

In this paper, fuzzy lower approximation-based fuzzy rough set is used for selection of features. A distributed sampling (DS)-based initialization method is introduced to pick better seed population, in particle swarm optimization (PSO) and intelligent dynamic swarm (IDS). PSO and IDS are used for simultaneously selecting the appropriate feature subset. Fitness function for these computations is fuzzy rough dependency measure. Using the proposed initialization, while using PSO and IDS, improvement in size of selected subset of features with improved classification accuracy is also demonstrated. © 2019, Springer Nature Singapore Pte Ltd.