期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 137, 期 -, 页码 46-58出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2019.06.044
关键词
Cost-sensitive feature selection; Artificial bee colony algorithm; Multi-objective optimization; Particle swarm optimization; Differential evolution
类别
资金
- National Natural Science Foundation of China [61876185, 61806119, 61773119, 61703256, 61771297, 61761136008]
- Six Talent Peaks Project in Jiangsu Province [DZXX-053]
- Natural Science Basic Research Plan in Shaanxi Province of China [2019JM-320]
- Fundamental Research Funds for the Central Universities
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. (C) 2019 Elsevier Ltd. All rights reserved.
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