Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm. (15th December 2019)

• Record Type: Journal Article
• Authors: Zhang, Yong; Cheng, Shi; Shi, Yuhui; Gong, Dun-wei; Zhao, Xinchao
• Is Part Of: Expert systems with applications. Volume 137(2019)
• Page Start: 46
• Page End: 58
• Publication Date: 2019-12-15
• Subjects: Cost-sensitive feature selection -- Artificial bee colony algorithm -- Multi-objective optimization -- Particle swarm optimization -- Differential evolution; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.06.044
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A two-archive-guided multiobjective artificial bee colony algorithm was designed. The algorithm's convergence and exploitation abilities are enhanced. Two archives are employed to enhance the search capability of the algorithm. Results have shown that TMABC-FS is an efficient and robust optimization method. Abstract: Since different features may require different costs, the cost-sensitive feature selection problem become more and more important in real-world applications. Generally, it includes two main conflicting objectives, i.e., maximizing the classification performance and minimizing the feature cost. However, most existing approaches treat this task as a single-objective optimization problem. To satisfy various requirements of decision-makers, this paper studies a multi-objective feature selection approach, called two-archive multi-objective artificial bee colony algorithm (TMABC-FS). Two new operators, i.e., convergence-guiding search for employed bees and diversity-guiding search for onlooker bees, are proposed for obtaining a group of non-dominated feature subsets with good distribution and convergence. And two archives, i.e., the leader archive and the external archive are employed to enhance the search capability of different kinds of bees. The proposed TMABC-FS is validated on several datasets from UCI, and is compared with two traditional algorithms and three multi-objective methods. Results have shown that TMABC-FS is an efficient and robust optimization method for solving cost-sensitive feature selection problems.