The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems
Open accessAbstract
Abstract Searching for the (near) optimal subset of features is a challenging problem in the process of feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior performance in solving this problem. This motivated our attempts to test the performance of the newly proposed Salp Swarm Algorithm (SSA) in this area. As such, two new wrapper FS approaches that use SSA as the search strategy are proposed. In the first approach, eight transfer functions are employed to convert the continuous version of SSA to binary. In the second approach, the crossover operator is used in addition to the transfer functions to replace the average operator and enhance the exploratory behavior of the algorithm. The proposed approaches are benchmarked on 22 well-known UCI datasets and the results are compared with 5 FS methods: Binary Grey Wolf Optimizer (BGWO), Binary Gravitational Search Algorithms (BGSA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Genetic Algorithm (GA). The paper also considers an extensive study of the parameter setting for the proposed technique. From the results, it is observed that the proposed approach significantly outperforms others on around 90% of the datasets.
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Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
A new optimizer using particle swarm theory
Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Theoretical and Empirical Analysis of ReliefF and RReliefF
Robnik-Šikonja, M., & Kononenko, I. (2003) · Machine Learning · doi:10.1023/a:1025667309714 · Cited by 3,025 · cited 2×
“Filter methods (Chi-Square [3], Information Gain [4], Gain Ratio [5], ReliefF [6]) rely on some data properties without involving a specific learning algorithm.”
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Cited by
Binary biogeography-based optimization based SVM-RFE for feature selection
Albashish, D., Hammouri, A. I., Braik, M., Atwan, J., & Sahran, S. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2020.107026 · Cited by 154
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
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Metadata
- Authors
- Faris, H., Mafarja, M. M., Heidari, A. A., Aljarah, I., Al-Zoubi, A. M., Mirjalili, S., & Fujita, H.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 154
- Pages
- 43-67
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2801536506
- Cited by
- 660
- References
- 4
- FWCI
- 47.53
- Type
- journal article
- Language
- en
- Open access
- green
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- Faris, H., Mafarja, M. M., Heidari, A. A., Aljarah, I., Al-Zoubi, A. M., Mirjalili, S., & Fujita, H. (2018). An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2018.05.009
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