A Hybrid PSO with Rao Algorithm for Classification of Wisconsin Breast Cancer Dataset

Abstract

This paper proposes a hybrid PSO algorithm with Rao algorithms for breast cancer data classification. In this study, the proposed algorithm improved the process of updating the velocity, and the new position of particle was selected from the best particle classification of efficiency. The Rao algorithm was applied to update the velocity and the new position of particle was selected from the best particle classification of efficiency values. The algorithm was evaluated on Wisconsin Breast Cancer Dataset (WBCD) from UCI Machine Learning Repository. The computational results of the proposed algorithm were compared with PSO, J48, SMO, Random Forest, Multilayer perceptron, and Naïve Bayes. The results of the comparison revealed that the proposed algorithm showed high performance for classification at 98%, better than PSO, J48, SMO, Random Forest, and Multilayer perceptron, and Naive Bayes.