Hybrid feature selection method for supervised classification based on Laplacian score ranking
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
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Microarray datasets comprise a large number of gene expression values and a relatively small number of samples. Feature selection algorithms are very useful in these situations in order to find a compact subset of informative features. We propose a redundancy control method for algorithms in the recently proposed SPEC family of spectral-based feature selection algorithms. This method is applied to find relevant genes in order to cluster samples corresponding to three kinds of cancer: lung, breast and colon.