Local linear multi-SVM method for gene function classification

Benhui Chen*, Feiran Sun, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

16 被引用数 (Scopus)

抄録

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.

本文言語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
国/地域Japan
CityKitakyushu
Period10/12/1510/12/17

ASJC Scopus subject areas

  • 計算理論と計算数学
  • 理論的コンピュータサイエンス

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