A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection. (9th November 2020) - Chu, Xianghua; Li, Shuxiang; Gao, Da; Zhao, Wei; Cui, Jianshuang; Huang, Linya
Abstract : This paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension. A dynamic Cauchy mutation is introduced to diversify the population distribution. Ten datasets from UCI repository are adopted as test problems, and the average results of cross-validation of BSTABC-DCM are compared with other seven popular swarm intelligence metaheuristics. Experimental results demonstrate that BSTABC-DCM could obtain the optimal classification accuracy and select the best representative features for the UCI problems.