Feature selection in proteomic pattern data with support vector machines

This work introduces novel methods for feature selection (FS) based on support vector machines (SVM). The methods combine feature subsets produced by a variant of SVM-RFE, a popular feature ranking/selection algorithm based on SVM. Two combination strategies are proposed: union of features occurring frequently, and ensemble of classifiers built on single feature subsets. The resulting methods are applied to pattern proteomic data for tumor diagnostics. Results of experiments on three proteomic pattern datasets indicate that combining feature subsets affects positively the prediction accuracy of both SVM and SVM-RFE. A discussion about the biological interpretation of selected features is provided.