Experimental Study of the Instance Sampling Effect on Feature Subset Selection Using Permutational-Based Differential Evolution
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Springer Nature Switzerland
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https://link.springer.com/content/pdf/10.1007/978-3-031-51940-6_31
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2. Barradas-Palmeros, J.A., Mezura-Montes, E., Acosta-Mesa, H.G., Rivera-López, R.: Fitness function comparison for unsupervised feature selection with permutational-based differential evolution. In: Rodríguez-González, A.Y., Pérez-Espinosa, H., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds.) Pattern Recognition. MCPR 2023. LNCS, vol. 13902, pp. 58–68. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-33783-3_6
3. Brest, J., Maučec, M.S., Bošković, B.: iL-shade: improved l-shade algorithm for single objective real-parameter optimization. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 1188–1195 (2016). https://doi.org/10.1109/CEC.2016.7743922
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5. Engelbrecht, A.P., Grobler, J., Langeveld, J.: Set based particle swarm optimization for the feature selection problem. Eng. Appl. Artif. Intell. 85, 324–336 (2019). https://doi.org/10.1016/j.engappai.2019.06.008
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1. Computational Cost Reduction in Wrapper Approaches for Feature Selection: A Case of Study Using Permutational-Based Differential Evolution;2024 IEEE Congress on Evolutionary Computation (CEC);2024-06-30
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