Composite kernel based SVM for hierarchical multi-label gene function classification

Benhui Chen*, Lihua Duan, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

This paper proposes a hierarchical multi-label classification method based on SVM with composite kernel for solving gene function prediction. The hierarchical multi-label classification problem is resolved into a set of binary classification tasks. A composite kernel based SVM (ck-SVM) is introduced to deal with the binary classification tasks. In estimation procedure of ck-SVM, a supervised clustering with over-sampling strategy is introduced for solving imbalance dataset learning problem and improve classification performance. 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2012 International Joint Conference on Neural Networks, IJCNN 2012
DOI
出版ステータスPublished - 2012 8 22
イベント2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
継続期間: 2012 6 102012 6 15

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks

Conference

Conference2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
国/地域Australia
CityBrisbane, QLD
Period12/6/1012/6/15

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

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