Computational Intelligence Optimization Algorithm Based on Meta-heuristic Social-Spider: Case Study on CT Liver Tumor Diagnosis

Abstract

Abstract-Feature selection is an importance step in classification phase and directly affects the classification performance. Feature selection algorithm explores the data to eliminate noisy, redundant, irrelevant data, and optimize the classification performance. This paper addresses a new subset feature selection performed by a new Social Spider Optimizer algorithm (SSOA) to find optimal regions of the complex search space through the interaction of individuals in the population. SSOA is a new natural meta-heuristic computation algorithm which mimics the behavior of cooperative social-spiders based on the biological laws of the cooperative colony. Different combinatorial set of feature extraction is obtained from different methods in order to keep and achieve optimal accuracy. Normalization function is applied to smooth features between [0,1] and decrease gap between features. SSOA based on feature selection and reduction compared with other methods over CT liver tumor dataset, the proposed approach proves better performance in both feature size reduction and classification accuracy. Improvements are observed consistently among 4 classification methods. A theoretical analysis that models the number of correctly classified data is proposed using Confusion Matrix, Precision, Recall, and Accuracy. The achieved accuracy is 99.27%, precision is 99.37%, and recall is 99.19%. The results show that, the mechanism of SSOA provides very good exploration, exploitation and local minima avoidance.

Derived Multi-population Genetic Algorithm for Adaptive Fuzzy C-Means Clustering

“…We use three standard test functions as shown in Equation ( 13), (14), and ( 15) are chosen to perform the optimization test to verify the performance of DMGA and its effectiveness. Among them, 1 x y , (15) The 3D image of the surface of the test function is shown in Figure 6.…”

“…Anter et al [13] also proposed a version of the FFCM, that uses the crow search optimization algorithm (CSA) to find the center of mass of the clusters during the clustering process to obtain more accurate results. ElSoud et al [14] proposed a new subset feature selection method performed by the new Social Spider Optimizer algorithm (SSOA) that is able to find the optimal region of a complex search space by the interaction of individuals in the population. Shi Y et al [15] propose a novel update equation and an improved dimension-selection strategy for bee colony optimization to get a more balanced search for superiority.…”