Local linear multi-SVM method for gene function classification

Benhui Chen, Feiran Sun, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

This paper proposes a local linear multi-SVM method based on composite kernel for solving classification tasks in gene function prediction. The proposed method realizes a nonlinear separating boundary by estimating a series of piecewise linear boundaries. Firstly, according to the distribution information of training data, a guided partitioning approach composed of separating boundary detection and clustering technique is used to obtain local subsets, and each subset is utilized to capture prior knowledge of corresponding local linear boundary. Secondly, a composite kernel is introduced to realize the local linear multi-SVM model. Instead of building multiple local SVM models separately, the prior knowledge of local subsets is used to construct a composite kernel, then the local linear multi-SVM model is realized by using the composite kernel exactly in the same way as a single SVM model. Experimental results on benchmark datasets demonstrate that the proposed method improves the classification performance efficiently.

Original languageEnglish
Title of host publicationProceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010
Pages183-188
Number of pages6
DOIs
Publication statusPublished - 2010
Event2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010 - Kitakyushu
Duration: 2010 Dec 152010 Dec 17

Other

Other2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010
CityKitakyushu
Period10/12/1510/12/17

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Keywords

  • Composite kernel
  • Gene function classification
  • Local linear
  • Multi-SVM model
  • Prior knowledge

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Chen, B., Sun, F., & Furuzuki, T. (2010). Local linear multi-SVM method for gene function classification. In Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010 (pp. 183-188). [5716332] https://doi.org/10.1109/NABIC.2010.5716332