J Integr Bioinform - On the parameter optimization of Support Vector Machines for binary classification.

Tópicos

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Resumo

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.

Resumo Limpo

classifi biolog data common task biomed context predict class new unknown inform allow research gain insight make decis base avail data also use classif method often impli choos best paramet obtain optim class separ number paramet might larg biolog dataset support vector machin provid wellestablish power classif method analys data find minimalrisk separ differ class find separ strong depend avail featur set tune hyperparamet techniqu featur select svm paramet optim known improv classif accuraci literatur extens paper review strategi use improv classif perform svms perform experiment studi influenc featur hyperparamet optim process use sever known kernel

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