On the parameter optimization of Support Vector Machines for binary classification

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doi doi:10.2390/biecoll-jib-2012-201
submission July 02, 2012
published July 24, 2012
NCBI PubMed PubMed ID 22829572

Paulo Gaspar, Jaime Carbonell and José Luís Oliveira

Correspondence should be addressed to:
José Luís Oliveira
IEETA, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal
tp.au@nullolj


Abstract

Classifying biological data is a common task in the biomedical context. Predicting the class of new, unknown information allows researchers to gain insight and make decisions based on the available data. Also, using classification methods often implies choosing the best parameters to obtain optimal class separation, and the number of parameters might be large in biological datasets. Support Vector Machines provide a well-established and powerful classification method to analyse data and find the minimal-risk separation between different classes. Finding that separation strongly depends on the available feature set and the tuning of hyper-parameters. Techniques for feature selection and SVM parameters optimization are known to improve classification accuracy, and its literature is extensive. In this paper we review the strategies that are used to improve the classification performance of SVMs and perform our own experimentation to study the influence of features and hyper-parameters in the optimization process, using several known kernels.

Reference

Paulo Gaspar, Jaime Carbonell and José Luís Oliveira. On the parameter optimization of Support Vector Machines for binary classification. Journal of Integrative Bioinformatics, 9(3):201, 2012. Online Journal: http://journal.imbio.de/index.php?paper_id=201
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