Feature selection with SVM-RFE

版本 1.3.0.0 (5.4 KB) 作者: Ke Yan
Support vector machine recursive feature elimination (SVM-RFE), with correlation bias reduction
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更新时间 2015/9/13

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SVM-RFE is a powerful feature selection algorithm in bioinformatics. It is a good choice to avoid overfitting when the number of features is high.
However, it may be biased when there are highly correlated features. We propose a "correlation bias reduction" strategy to handle it. See our paper (Yan et al., Feature selection and analysis on correlated gas sensor data with recursive feature elimination", 2015).
This file is an implementation of both our method and the original SVM-RFE, including the linear and RBF kernel. **LibSVM is needed**
Thanks to the SVM-KM and spider toolbox!

引用格式

Ke Yan (2024). Feature selection with SVM-RFE (https://www.mathworks.com/matlabcentral/fileexchange/50701-feature-selection-with-svm-rfe), MATLAB Central File Exchange. 检索来源 .

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版本 已发布 发行说明
1.3.0.0

1. remove "sv_indices" in function trainSVM older versions of libSVM don't have it
2. add a simple support for multi-class problems

1.2.0.0

fixed a bug: changed
if isempty(model) || model.nSV == 0
to
if isempty(model) || sum(model.nSV) == 0

1.1.0.0

revise description

1.0.0.0