[读论文]A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification（2021）

一种用于非线性支持向量机分类中特征选择的新型嵌入式最小-最大方法
Asunci′ on Jim′ enez-Cordero

DOI: 10.1016/j.ejor.2020.12.009

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摘要：

In recent years, feature selection has become a challenging problem in several machine learning fifields, particularly in classifification problems. Support Vector Machine (SVM) is a well-known technique applied in (nonlinear) classifification.Various methodologies have been proposed in the literature to select the most

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