Abstract
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.
Feature extraction and selection is an importance step In classification phase and directly affects the classification performance. Feature selection algorithm explores the data to eliminate noisy, Irrelevant, redundant data, and simultaneously optimize the classification performance. 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.