Nearest mean classification via one-class SVM

Donghyuk Shin, Saejoon Kim

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

4 Scopus citations

Abstract

We propose a new multi-class classification algorithm based on one-class SVM and nearest mean classifier methods. A wrapper-style feature selection scheme designed specifically for our algorithm is also provided for increased classification accuracy. It will be demonstrated that the proposed classification algorithm provide excellent performance, and in particular, performs strictly better than some of the currently known best classification algorithms on five biological datasets.

Original languageEnglish (US)
Title of host publicationProceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, CSO 2009
Pages593-596
Number of pages4
DOIs
StatePublished - 2009
Event2009 International Joint Conference on Computational Sciences and Optimization, CSO 2009 - Sanya, Hainan, China
Duration: Apr 24 2009Apr 26 2009

Publication series

NameProceedings of the 2009 International Joint Conference on Computational Sciences and Optimization, CSO 2009
Volume1

Conference

Conference2009 International Joint Conference on Computational Sciences and Optimization, CSO 2009
CountryChina
CitySanya, Hainan
Period4/24/094/26/09

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Nearest mean classification via one-class SVM'. Together they form a unique fingerprint.

Cite this