Role of Hybrid Evolutionary Approaches for Feature Selection in Classification: A Review
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Springer International Publishing
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https://link.springer.com/content/pdf/10.1007/978-3-031-11713-8_10
Reference41 articles.
1. Liu, H., Zhao, Z.: Manipulating data and dimension reduction methods: Feature selection. In: Encyclopedia of Complexity and Systems Science, pp. 5348–5359, Springer (2009). https://doi.org/10.1007/978-0-387-30440-3_317
2. Piri, J., Dey, R.: Quantitative association rule mining using multi-objective particle swarm optimization. Int J Sci Eng Res 5(10), 155–161 (2014)
3. Bindu, M.G., Sabu, M.K.: A hybrid feature selection approach using artifcial bee colony and genetic algorithm. 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), Cochin, India, pp. 211-216 (2020). https://doi.org/10.1109/ACCTHPA49271.2020.9213197
4. Ajibade, S.-S.M., Binti Ahmad, N.B., Zainal, A.: A hybrid chaotic particle swarm optimization with differential evolution for feature selection. 2020 IEEE Symposium on Industrial Electronics & Applications (ISIEA), TBD, Malaysia, pp. 1-6 (2020). https://doi.org/10.1109/ISIEA49364.2020.9188198
5. Ahmed, S., Ghosh, K.K., Singh, P.K., Geem, Z.W., Sarkar, R.: Hybrid of harmony search algorithm and ring theory-based evolutionary algorithm for feature selection. IEEE Access 8, 102629–102645 (2020). https://doi.org/10.1109/ACCESS.2020.2999093
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1. Literature Review on Hybrid Evolutionary Approaches for Feature Selection;Algorithms;2023-03-20
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