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Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm
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DOI：10.1016/j.eswa.2019.06.044.png)
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摘要
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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.
Keyword:
Cost-sensitive feature selection
Artificial bee colony algorithm
Multi-objective optimization
Particle swarm optimization
Differential evolution
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7.5
论文数:
2.9W
被引数:
10.2W
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Multi-criteria feature selection on cost-sensitive data with missing values.png)
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