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

Benhui Chen*, Jinglu Hu

*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
Pages231-236
Number of pages6
DOIs
Publication statusPublished - 2010 Dec 1
Event2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 - Cairo, Egypt
Duration: 2010 Nov 292010 Dec 1

Publication series

NameProceedings 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
Country/TerritoryEgypt
CityCairo
Period10/11/2910/12/1

Keywords

  • Consistency ensemble
  • Gene function prediction
  • Hierarchical SMOTE
  • Hierarchical multi-label classification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture

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