Local linear multi-SVM method for gene function classification

Benhui Chen, Feiran Sun, Jinglu Hu

研究成果: Conference contribution

14 引用 (Scopus)

抜粋

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.

元の言語English
ホスト出版物のタイトルProceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010
ページ183-188
ページ数6
DOI
出版物ステータスPublished - 2010 12 1
イベント2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010 - Kitakyushu, Japan
継続期間: 2010 12 152010 12 17

出版物シリーズ

名前Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010

Conference

Conference2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010
Japan
Kitakyushu
期間10/12/1510/12/17

    フィンガープリント

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

これを引用

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