Support Vector Machine ensembles using features distribution among subsets for enhancing microarray data classification

Eman Ahmed, Neamat El-Gayar, Iman A. El-Azab

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

1 Citation (Scopus)

Abstract

Support Vector Machines (SVMs) ensembles have been widely used to improve classification accuracy in complicated pattern recognition tasks. In this work we propose to apply an ensemble of SVMs coupled with feature-subset selection methods to aleviate the curse of dimensionality associated with expression-based classification of DNA microarray data. We compare the single SVM classifier to SVM ensembles applying two different feature-subset selection techniques, namely random selection and k-means clustering, the base classifiers are combined using either majority vote or SVM fusion. Two real-world benchmarks datasets are used to evaluate and compare the performance. Experimental results show that the SVM ensemble of SVM base classifiers using k-means clustering for feature-subset selection and employing an SVM combiner achieve the best classification accuracy, and that feature-subset-selection methods can have considerable impact on the classification acuracy.

Original languageEnglish
Title of host publication2010 10th International Conference on Intelligent Systems Design and Applications
PublisherIEEE
Pages1242-1246
Number of pages5
ISBN (Electronic)9781424481361
ISBN (Print)9781424481354
DOIs
Publication statusPublished - 13 Jan 2011
Event10th International Conference on Intelligent Systems Design and Applications 2010 - Cairo, Egypt
Duration: 29 Nov 20101 Dec 2010

Conference

Conference10th International Conference on Intelligent Systems Design and Applications 2010
Abbreviated titleISDA'10
CountryEgypt
CityCairo
Period29/11/101/12/10

Keywords

  • Ensemble classification
  • Feature selection
  • Feature subsets
  • Microarray data
  • Support Vector Machines (SVM)
  • SVM fusion

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture

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