Feature selection with SVM-RFE

Version 1.3.0.0 (5,42 KB) von Ke Yan
Support vector machine recursive feature elimination (SVM-RFE), with correlation bias reduction
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Aktualisiert 13. Sep 2015

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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!

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Ke Yan (2025). Feature selection with SVM-RFE (https://www.mathworks.com/matlabcentral/fileexchange/50701-feature-selection-with-svm-rfe), MATLAB Central File Exchange. Abgerufen.

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Version Veröffentlicht Versionshinweise
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