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On the parameter optimization of Support Vector Machines for binary classification
Gaspar, Paulo ; Carbonell, Jaime ; Oliveira, Jose Luis
Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516)
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.
||Faculty of Technology, Research Groups in Informatics
||Data processing, computer science, computer systems
On the parameter optimization of Support Vector Machines for binary classification.
Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516), 9(3): Special Issue: Selected extended papers of the 6th International Conference on Practical Applications of Computational Biology and Bioinformatics, Salamanca, Spain, 2012