A method for feature selection on microarray data using support vector machine
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 4081 LNCS, Page: 513-523
2006
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Conference Paper Description
The data collected from a typical microarray experiment usually consists of tens of samples and thousands of genes (i.e., features). Usually only a small subset of features is relevant and non-redundant to differentiate the samples. Identifying an optimal subset of relevant genes is crucial for accurate classification of samples. In this paper, we propose a method for relevant gene subset selection for microarray gene expression data. Our method is based on gap tolerant classifier, a variation of support vector machine, and uses a hill-climbing search strategy. Unlike most other hill-climbing approaches, where classification accuracies are used as a criterion for feature selection, the proposed method uses a mixture of accuracy and SVM margin to select features. Our experimental results show that this strategy is effective both in selecting relevant and in eliminating redundant features. © Springer-Verlag Berlin Heidelberg 2006.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=33751360766&origin=inward; http://dx.doi.org/10.1007/11823728_49; http://link.springer.com/10.1007/11823728_49; https://dx.doi.org/10.1007/11823728_49; https://link.springer.com/chapter/10.1007/11823728_49; http://www.springerlink.com/index/10.1007/11823728_49; http://www.springerlink.com/index/pdf/10.1007/11823728_49
Springer Science and Business Media LLC
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