Gene selection and classification by entropy-based recursive feature elimination

We analyse E-RFE (entropy-based recursive feature elimination), a new wrapper algorithm for fast feature ranking in classification problems. The E-RFE method operates the elimination of chunks of uninteresting features according to the entropy of the weights distribution of a SVM classifier. The method is designed to support computationally intensive model selection in classification problems in which the number of features is much larger than the number of samples. We test the elimination procedure on synthetic data sets, and we demonstrate the applicability of E-RFE for the identification of biomedically important genes in predictive classification of microarray data.