Journal
INFORMATION SCIENCES
Volume 418, Issue -, Pages 561-574Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2017.08.047
Keywords
Firefly algorithm; Feature selection; Return-cost; Pareto dominance; Binary movement
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Funding
- National Natural Science Foundation of China [61473299, 61473298, 61573361, 61375067, 61673404]
- National Basic Research Program of China (973 Program) [2014CB046306-2]
- Outstanding Innovation Team of China University of Mining and Technology [2015QN003]
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Various real-world applications can be formulated as feature selection problems, which have been known to be NP-hard. In this paper, we propose an effective feature selection method based on firefly algorithm (FFA), called return-cost-based binary FFA (Rc-BBFA). The proposed method has the capability of preventing premature convergence and is particularly efficient attributed to the following three aspects. An indicator based on the return-cost is first defined to measure a firefly's attractiveness from other fireflies. Then, a Pareto dominance-based strategy is presented to seek the attractive one for each firefly. Finally, a binary movement operator based on the return-cost attractiveness and the adaptive jump is developed to update the position of a firefly. The experimental results on a series of public datasets show that the proposed method is competitive in comparison with other feature selection algorithms, including the traditional algorithms, the GA-based algorithm, the PSO-based algorithm, and the FFA-based algorithms. (C) 2017 Published by Elsevier Inc.
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