A Sparse Twin SVM for multi-classification problems

HONG-XING YAO; XIAO-WEI LIU

doi:https://doi.org/10.14445/22315373/IJMTT-V4I3P501

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 4 | Issue 3 | Year 2013 | Article Id. IJMTT-V4I3P501 | DOI : https://doi.org/10.14445/22315373/IJMTT-V4I3P501

A Sparse Twin SVM for multi-classification problems

HONG-XING YAO, XIAO-WEI LIU

Abstract

We propose Sparse TSVM, a multi-class SVM classifier that determines k nonparallel planes by solving k related SVM-type problems. The Sparse TSVM promotes Twin SVM to one-versus-rest approach. And it capture classes' main feature better with the sparse algorithm. On several benchmark data sets, Sparse TSVM is not only fast, but shows good generalization.

Keywords

Data Mining, pattern classification, machine learning, sparse, Twin support vector machine.

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Citation :

HONG-XING YAO, XIAO-WEI LIU, "A Sparse Twin SVM for multi-classification problems," International Journal of Mathematics Trends and Technology (IJMTT), vol. 4, no. 3, pp. 41-52, 2013. Crossref, https://doi.org/10.14445/22315373/IJMTT-V4I3P501