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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Hlavní autoři:	Yihui Qiu, Ruoyu Li, Xinqiang Zhang
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2024-01-01
Edice:	IEEE Access
Témata:	Feature selection slime mould algorithm support vector machine parameter optimization metaheuristic algorithm
On-line přístup:	https://ieeexplore.ieee.org/document/10384886/
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