Feature Selection Approach Based on Social Spider Algorithm: Case Study on Abdominal CT Liver Tumor

This paper addresses a new subset feature selection performed by new Social Spider Optimization algorithm (SSOA) to find optimal regions of the complex search space through the interaction of individuals in the population. SSOA is a new evolutionary computation technique which mimics the behavior of cooperative social-spiders based on the biological laws of the cooperative colony. The performance of SSOA associated with two reasons: (a) operators allow to increasing find the global optima in the search space, and (b) division of the population into male and female, provides the use of different rates between exploration and exploitation during the evolution process. A theoretical analysis on abdominal CT liver tumor dataset that models the number of correctly classified data is proposed using Confusion Matrix, Precision, Recall, and accuracy. The results show that the mechanism of SSOA provides very good exploration, local minima avoidance, and exploitation simultaneously.