Abstract
With the ongoing advancement of big data and information technology, the efficient extraction of valuable feature information from vast existing datasets has become a fundamental task. The task is called feature selection, which is of paramount importance in contemporary data mining. It can eliminate irrelevant or redundant features and select the most relevant and useful features from the raw data to improve the model's generalization ability and accuracy. This process helps reduce modeling costs and shorten execution time. In this context, a multi-objective feature selection problem is proposed with the objectives of minimizing both the number of features and the classification error rate. To address this multi-objective problem more effectively, this paper designs a modified variable velocity strategy particle swarm optimization algorithm. The algorithm incorporates whale encircling and flipping, along with an inertia weight updating strategy for random perturbation, known as WETVVS-MOPSO. The results show that WETVVS-MOPSO significantly outperforms its competitors.
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References
Kira, K., et al.: The feature selection problem: traditional methods and a new algorithm. In: Proceedings of the Tenth National Conference on Artificial intelligence, pp. 129–134 (1992)
Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(1–4), 131–156 (1997)
Sumathi, S., et al.: Evolutionary intelligence: an introduction to theory and applications with Matlab. Springer Science & Business Media (2008)
Kennedy, J., et al.: Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
Sadeghian, Z., et al.: A review of feature selection methods based on meta-heuristic algorithms. J. Exper. Theor. Artific. Intell. 1–51 (2023)
Abualigah, L.M., et al.: Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J. Supercomput. 73, 4773–4795 (2017)
Rodrigues, D., et al.: A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Syst. Appl. 41(5), 2250–2258 (2014)
Zhang, M., et al.: A review of research on feature selection for multi-objective optimization. Chin. J. Comput. Eng. Appl. 59(3) (2023)
Aljarah, I., et al.: A dynamic locality multi-objective salp swarm algorithm for feature selection. Comput. Ind. Eng. 147, 106628 (2020)
Dokeroglu, T., et al.: A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing 494, 269–296 (2022)
Joseph Manoj, R., et al.: An ACO–ANN based feature selection algorithm for big data. Clust. Comput. 22, 3953–3960 (2019)
Niu, B., et al.: A multi-objective feature selection method based on bacterial foraging optimization. Natl. Comput. 1–14 (2019)
Shi, Y., et al.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 3, pp. 1945–1950, IEEE (1999)
Minh, H.L., et al.: A variable velocity strategy particle swarm optimization algorithm for damage assessment in structures. Eng. Comput. 39(2), 1055–1084 (2023)
Wang, J., et al.: A hybrid particle swarm optimization algorithm with dynamic adjustment of inertia weight based on a new feature selection method to optimize SVM parameters. Entropy 25(3), 531 (2023)
Zhang, J., et al.: UCPSO: A uniform initialized particle swarm optimization algorithm with cosine inertia weight. Comput. Intell. Neurosci. (2021)
Mirjalili, S., et al.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Yu, J., et al.: Whale algorithm based on nonlinear convergence factor and local perturbation. Comput. Eng. Design 10 (2019)
Yi, W.: Research on bacterial-inspired multi-objective feature selection method in customer segmentation. Shenzhen University, In Chinese (2019)
UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets.html. Accessed 5 Feb 2024
Schott, J.R.: Fault tolerant design using single and multicriteria genetic algorithm optimization. Massachusetts Institute of Technology (1995)
Wang, L., et al.: A review of research on performance evaluation metrics for multi-objective evolutionary algorithms. Chin. J. Comput. 44(8), 1590–1619 (2021)
Acknowledgement
This work is supported by Shenzhen Higher Education Support Plan (Project No. 20231120174835002) and National Natural Science Foundation of China (Project No.72334004).
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Liu, X., Niu, B., Yi, W. (2024). A Modified Variable Velocity Strategy Particle Swarm Optimization Algorithm for Multi-objective Feature Selection. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2024. Lecture Notes in Computer Science, vol 14788. Springer, Singapore. https://doi.org/10.1007/978-981-97-7181-3_4
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DOI: https://doi.org/10.1007/978-981-97-7181-3_4
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