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- J. Mohr, S. Seo, and
K. Obermayer. Automated microarray classification based on P-SVM gene
selection.
.
In Proceedings of the ICMLA '08: The Seventh International Conference on
Machine Learning and Applications, pages 503-507, 2008.
The first two authors contributed equally.
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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