Simultaneous SVM Parameters and Feature Selection Optimization Based on Improved Slime Mould Algorithm

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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Bibliographic Details
Main Authors: Yihui Qiu, Ruoyu Li, Xinqiang Zhang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Feature selection
slime mould algorithm
support vector machine
parameter optimization
metaheuristic algorithm
Online Access:https://ieeexplore.ieee.org/document/10384886/

Internet

https://ieeexplore.ieee.org/document/10384886/

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