Automated Microarray Classification Based on P-SVM Gene Selection

The analysis of microarray data is a challenging task for statistical and machine learning methods, since the datasets usually contain a very large number of features (genes) and only a small number of examples (subjects). In this work, we describe a technique for gene selection and classification of microarray data based on the recently proposed potential support vector machine (P-SVM) for feature selection and a nu-SVM for classification. The P-SVM expands the decision function in terms of a sparse set of "support features". Based on this novel technique for feature selection, we suggest a fully automated method for gene selection, hyper-parameter optimization and microarray classification. Benchmark results are given for the two datasets provided by the ICMLA\´08 Automated Micro-Array Classification Challenge.