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Adaptive multi-class support vector machine for microarray classification and gene selection

  • Juntao Li*
  • , Yingmin Jia
  • , Junping Du
  • , Fashan Yu
  • *Corresponding author for this work
  • Beihang University
  • Beijing University of Posts and Telecommunications
  • Henan Polytechnic University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes an adaptive multi-class support vector machine for simultaneous microarray classification and gene selection. By evaluating the gene ranking significance, the adaptive multi-class support vector machine is shown to encourage an adaptive grouping effect in the process of building classifiers, thus leading a sparse multi-classifiers with enhanced interpretability. Based on a reasonable correlation between the two regularization parameters, an efficient solution path algorithm is developed for solving the proposed support vector machine. Experiments performed on the leukaemia data set are provided to verify the obtained results.

Original languageEnglish
Title of host publicationICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages2658-2663
Number of pages6
StatePublished - 2009
EventICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka, Japan
Duration: 18 Aug 200921 Aug 2009

Publication series

NameICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings

Conference

ConferenceICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
Country/TerritoryJapan
CityFukuoka
Period18/08/0921/08/09

Keywords

  • Gene selection
  • Microarray classification
  • Multi-class support vector machine (MSVM)
  • Solution path

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