期刊
INFORMATION SCIENCES
卷 422, 期 -, 页码 462-479出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2017.09.028
关键词
Feature selection; Classification; Multi-objective optimization; Artificial bee colony
资金
- Marsden Fund of New Zealand Government [VUW1209, VUW1509, VUW 1615]
- Huawei Industry Fund [E2880/3663]
- University Research Fund at Victoria University of Wellington [209862/3580, 213150/3662]
Feature selection has two major conflicting aims, i.e., to maximize the classification performance and to minimize the number of selected features to overcome the curse of dimensionality. To balance their trade-off, feature selection can be handled as a multi-objective problem. In this paper, a feature selection approach is proposed based on a new multi objective artificial bee colony algorithm integrated with non-dominated sorting procedure and genetic operators. Two different implementations of the proposed approach are developed: ABC with binary representation and ABC with continuous representation. Their performance are examined on 12 benchmark datasets and the results are compared with those of linear forward selection, greedy stepwise backward selection, two single objective ABC algorithms and three well-known multi-objective evolutionary computation algorithms. The results show that the proposed approach with the binary representation outperformed the other methods in terms of both the dimensionality reduction and the classification accuracy. (C) 2017 Elsevier Inc. All rights reserved.
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