Collaborative SVM parameters and feature selection optimization based on improved squirrel search algorithm

Jia Jiang; Jinbo Cai; Sihuan He

doi:10.1117/12.3010827

2 November 2023 Collaborative SVM parameters and feature selection optimization based on improved squirrel search algorithm

Jia Jiang, Jinbo Cai, Sihuan He

Author Affiliations +

Proceedings Volume 12919, International Conference on Electronic Materials and Information Engineering (EMIE 2023); 1291915 (2023) https://doi.org/10.1117/12.3010827
Event: 3rd International Conference on Electronic Materials and Information Engineering (EMIE 2023), 2023, Guangzhou,, China

Abstract

In order to improve the problem of low accuracy of traditional support vector machines in solving data classification problems, the SVM classification method and feature selection are co-optimized. In order to solve the problem that the global optimal solution cannot be effectively obtained due to non-determinism, polynomial completeness and large scale, an improved squirrel search algorithm (ISSA) is proposed, and the spatial variation and diffusion mechanism is introduced to improve the traditional SSA to achieve fast convergence, and the improved SSA is used to solve the SVM classification method and feature selection collaborative optimization problem. Then, the improved wolf pack algorithm is used to optimize the kernel function and feature selection at the same time, and the selected feature classification results are obtained. Then, by comparing a variety of different algorithms in sixteen sets of classical UCI datasets, they have significant advantages in different index evaluations, and the experimental results show that the proposed algorithm can process data more accurately and avoid redundant feature interference.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jia Jiang, Jinbo Cai, and Sihuan He "Collaborative SVM parameters and feature selection optimization based on improved squirrel search algorithm", Proc. SPIE 12919, International Conference on Electronic Materials and Information Engineering (EMIE 2023), 1291915 (2 November 2023); https://doi.org/10.1117/12.3010827

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