Construction of Fuzzy Classifiers by a Brain Storm Optimization Algorithm
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-09677-8_33
Reference24 articles.
1. Fernandez, A., Herrera, F., Cordon, O., Jesus, M.J., Marcelloni, F.: Evolutionary fuzzy systems for explainable artificial intelligence: Why, When, What for, and Where to? IEEE Comput. Intell. Mag. 14(1), 69–81 (2019). https://doi.org/10.1109/MCI.2018.2881645
2. Svetlakov, M.O., Hodashinsky, I.A.: Clustering-based rule generation methods for fuzzy classifier using Autonomous Data Partitioning algorithm. J. Phys. Conf. Ser. 1989(1), 012032 (2021). https://doi.org/10.1088/1742-6596/1989/1/012032
3. Hodashinsky, I., Sarin, K., Shelupanov, A., Slezkin, A.: Feature selection based on swallow swarm optimization for fuzzy classification. Symmetry 11(11), 1423 (2019). https://doi.org/10.3390/sym11111423
4. Lavygina, A., Hodashinsky, I.: Hybrid algorithm for fuzzy model parameter estimation based on genetic algorithm and derivative based methods. In: ECTA 2011 FCTA 2011 - Proceedings of the International Conference on Evolutionary Computation Theory and Applications and International Conference on Fuzzy Computation Theory and Applications, pp. 513–515 (2011). https://doi.org/10.13140/2.1.2994.6881
5. Xue, Y., Zhang, Q., Zhao, Y.: An improved brain storm optimization algorithm with new solution generation strategies for classification. Eng. Appl. Artif. Intell. 110, 104677 (2022). https://doi.org/10.1016/j.engappai.2022.104677
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