A wrapper approach for feature selection based on swarm optimization algorithm inspired from the behavior of social-spiders

In this paper, a proposed system for feature selection based on social spider optimization (SSO) is proposed. SSO is used in the proposed system as searching method to find optimal feature set maximizing classification performance and mimics the cooperative behavior mechanism of social spiders in nature. The proposed SSO algorithm considers two different search agents (social members) male and female spiders, that simulate a group of spiders with interaction to each other based on the biological laws of the cooperative colony. Depending on spider gender, each spider (individual) is simulating a set of different evolutionary operators of different cooperative behaviors that are typically found in the colony. The proposed system is evaluated using different evaluation criteria on 18 different datasets, which compared with two common search methods namely particle swarm optimization (PSO), and genetic algorithm (GA). SSO algorithm proves an advance in classification performance using different evaluation indicators.