Abstract
In this paper, a multi-objective feature selection method based on Grey Wolf Optimization (GWO), named BMOGWO-FS, is proposed. Specifically, this paper first introduces a binary multi-objective GWO (BMOGWO), considering that feature selection problem is a 0–1 integer programming. Then, on the basis of BMOGWO, a multi-objective feature selection method named BMOGWO-FS is proposed, aiming to minimize the number of selected features while maximizing classification accuracy. To validate the performance of BMOGWO-FS, six different classifiers are employed, and a comparative analysis is conducted against six existing heuristic algorithms. Experimental results demonstrate that BMOGWO-FS achieves the best performance while maintaining good robustness and stability. Furthermore, BMOGWO-FS is applied to a real-world dataset of endometrial cancer. The experimental results show a significant improvement in the accuracy of predicting endometrial cancer recurrence after employing the BMOGWO-FS algorithm for feature selection.
This work was supported by the Natural Science Foundation of Zhejiang Province under Grant LZ20F010008.
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Jiang, Y., Jin, C., Zhang, Q., Hu, B., Tang, Z. (2024). A Binary Multi-objective Grey Wolf Optimization for Feature Selection. In: Cao, C., Chen, H., Zhao, L., Arshad, J., Asyhari, T., Wang, Y. (eds) Knowledge Science, Engineering and Management. KSEM 2024. Lecture Notes in Computer Science(), vol 14885. Springer, Singapore. https://doi.org/10.1007/978-981-97-5495-3_30
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DOI: https://doi.org/10.1007/978-981-97-5495-3_30
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