Feature selection with SVM-RFE

Version 1.3.0.0 (5,42 ko) par Ke Yan
Support vector machine recursive feature elimination (SVM-RFE), with correlation bias reduction
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Mise à jour 13 sept. 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!

Citation pour cette source

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

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Version Publié le Notes de version
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