Improving gene expression sample classification using support vector machine ensembles aggregated by boosting

Andrei Dragomir, Anastasios Bezerianos

Research output: Contribution to journalArticlepeer-review

Abstract

The molecular characterization of different tumor types using gene expression profiling is expected to uncover fundamental aspects related to cancer diagnosis and drug discovery. There is, therefore, a need for reliable, accurate sample classification tools, as well as methods for efficient identification of genes informative for the class discrimination. We propose a method based on Support Vector Machine (SVM) ensembles, trained within a boosting framework. The approach allows sequential training of classifiers on different data subsets, their aggregate yielding results superior to single SVM. Results from binary and multiclass classification experiments performed on several data sets are presented.

Original languageEnglish (US)
Pages (from-to)63-70
Number of pages8
JournalCancer Genomics and Proteomics
Volume3
Issue number1
StatePublished - Jan 1 2006

Keywords

  • Boosting
  • Classification
  • Ensemble learning
  • Feature selection
  • Gene expression
  • Support vector machines

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Cancer Research

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