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Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Ed. by Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Editorial Board: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan

4 Issues per year


CiteScore 2016: 0.93

SCImago Journal Rank (SJR) 2016: 0.416

Open Access
Online
ISSN
1613-4516
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Volume 9, Issue 3

Issues

On the parameter optimization of Support Vector Machines for binary classification

Paulo Gaspar
  • University of Aveiro, DETI/IEETA. Campus Universitário de Santiago, 3810 - 193 Aveiro, http://bioinformatics.ua.pt/, Portugal
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jaime Carbonell
  • Carnegie Mellon University, Language Technologies Institute. 5000 Forbes Avenue, Pittsburgh, PA 15213, United States of America
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ José Luís Oliveira
  • Corresponding author
  • University of Aveiro, DETI/IEETA. Campus Universitário de Santiago, 3810 - 193 Aveiro, http://bioinformatics.ua.pt/, Portugal
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2012-201

Summary

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.

About the article

Published Online: 2016-10-18

Published in Print: 2012-12-01


Citation Information: Journal of Integrative Bioinformatics, Volume 9, Issue 3, Pages 33–43, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2012-201.

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© 2012 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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