Simultaneous SVM Parameters and Feature Selection Optimization Based on Improved Slime Mould Algorithm
To address the problems of low classification accuracy, redundancy of feature subsets, and performance susceptibility to parameters in wrapper-based feature selection in traditional Support Vector Machine (SVM), an improved Slime Mould Algorithm (ISMA) was proposed for simultaneous optimization of S...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
IEEE
2024-01-01
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| Serie: | IEEE Access |
| Soggetti: | |
| Accesso online: | https://ieeexplore.ieee.org/document/10384886/ |
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