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Automated Microarray Classification Based on P-SVM Gene Selection
Citation key Mohr2008c
Author Mohr, J. and Seo, S. and Obermayer, K.
Title of Book Proceedings of the ICMLA '08: The Seventh International Conference on Machine Learning and Applications
Pages 503 – 507
Year 2008
ISBN 978-0-7695-3495-4
DOI 10.1109/ICMLA.2008.75
Publisher IEEE
Abstract 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.
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