The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Binary differential evolution with self-learning for multi-objective feature selection
Abstract
Abstract Feature selection is an important data preprocessing method. This paper studies a new multi-objective feature selection approach, called the Binary Differential Evolution with self-learning (MOFS-BDE). Three new operators are proposed and embedded into the MOFS-BDE to improve its performance. The novel binary mutation operator based on probability difference can guide individuals to rapidly locate potentially optimal areas, the developed One-bit Purifying Search operator (OPS) can improve the self-learning capability of the elite individuals located in the optimal areas, and the efficient non-dominated sorting operator with crowding distance can reduce the computational complexity of the selection operator in the differential evolution. Experimental results on a series of public datasets show that the effective combination of the binary mutation and OPS makes our MOFS-BDE achieve a trade-off between local exploitation and global exploration. The proposed method is competitive in comparison with some representative genetic algorithm-, particle swarm-, differential evolution-, and artificial bee colony-based feature selection algorithms.
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Citations
Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
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MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
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A Survey on Evolutionary Computation Approaches to Feature Selection
Xue, B., Zhang, M., Browne, W. N., & Yao, X. (2016) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2015.2504420 · Cited by 1,841
Particle swarm optimization for feature selection in classification: a multi-objective approach
Xue, B., Zhang, M., & Browne, W. N. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2227469 · Cited by 1,282
An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems
Faris, H., Mafarja, M. M., Heidari, A. A., Aljarah, I., Al-Zoubi, A. M., Mirjalili, S., & Fujita, H. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.05.009 · Cited by 660
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An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization
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Cited by
Differential Evolution: A Recent Review Based on State-of-the-Art Works
Ahmad, M. F., Isa, N. A. M., Lim, W. H., & Ang, K. M. (2022) · Alexandria Engineering Journal · doi:10.1016/j.aej.2021.09.013 · Cited by 579
Review of swarm intelligence-based feature selection methods
Rostami, M., Berahmand, K., Nasiri, E., & Forouzandeh, S. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104210 · Cited by 451
Song, X.-F., Zhang, Y., Gong, D.-W., & Gao, X.-Z. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3061152 · Cited by 348
Self-citationA novel community detection based genetic algorithm for feature selection
(2021) · doi:10.1186/s40537-020-00398-3 · Cited by 197
Mafarja, M., Thaher, T., Al-Betar, M. A., Too, J., Awadallah, M. A., Abu Doush, I., & Turabieh, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-04427-x · Cited by 91
Metadata
- Authors
- Zhang, Y., Gong, D.-W., Gao, X.-Z., Tian, T., & Sun, X.-Y.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 507
- Pages
- 67-85
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2897301007
- Cited by
- 429
- References
- 39
- FWCI
- 24.41
- Type
- journal article
- Language
- en
- Open access
- closed
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