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A new fuzzy support vectors machine for Biomedical Data classification

  • Silesian University of Technology

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

8 Citations (Scopus)

Abstract

In this paper a new approach to a fuzzy support vector machine (FSVM) for solving multi-class problems is presented. The developed algorithm combines two separate methods based on fuzzy support vector machine, one for solving two-class problems and the second for multi-class problems. The first method deals with the problem of selecting the best support vector machine (SVM) kernel function and the second method enables classification of unclassified regions that appear when classical SVM methods for solving multi-class problems are used. Presented tool has been subjected to the dataset from Kent Ridge Biomedical Data Set Repository and showed its superiority comparing with conventional SVM and FSVM methods.

Original languageEnglish
Title of host publicationProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
PublisherIEEE Computer Society
Pages4676-4679
Number of pages4
ISBN (Print)9781424418152
DOIs
Publication statusPublished - 2008
Event30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada
Duration: 20 Aug 200825 Aug 2008

Publication series

NameProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"

Conference

Conference30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
Country/TerritoryCanada
CityVancouver, BC
Period20/08/0825/08/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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