Hierarchical multi-label classification incorporating prior information for gene function prediction

Benhui Chen*, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

This paper proposes an improved Hierarchical Multi-label Classification (HMC) method for solving the gene function prediction. The HMC task is transferred into a series of binary SVM classification tasks. By introducing the hierarchy constraint into learning procedures, two measures with incorporating prior information are implemented to improve the HMC performance. Firstly, for imbalanced functional classes, a hierarchical SMOTE is proposed as over-sampling preprocessing to improve the SVM learning performance. Secondly, an improved True Path Rule consistency approach is introduced to ensemble the results of binary probabilistic SVM classifications. It can improve the classification results and guarantee the hierarchy constraint of classes.

本文言語English
ホスト出版物のタイトルProceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
ページ231-236
ページ数6
DOI
出版ステータスPublished - 2010 12月 1
イベント2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 - Cairo, Egypt
継続期間: 2010 11月 292010 12月 1

出版物シリーズ

名前Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

Conference

Conference2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
国/地域Egypt
CityCairo
Period10/11/2910/12/1

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • ハードウェアとアーキテクチャ

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