Multiple classifier fusion using k-nearest localized templates

Jun Ki Min, Sung Bae Cho

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

1 Citation (Scopus)

Abstract

This paper presents a method for combining classifiers that uses knearest localized templates. The localized templates are estimated from a training set using C-means clustering algorithm, and matched to the decision profile of a new incoming sample by a similarity measure. The sample is assigned to the class which is most frequently represented among the k most similar templates. The appropriate value of k is determined according to the characteristics of the given data set. Experimental results on real and artificial data sets show that the proposed method performs better than the conventional fusion methods.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings
Pages447-456
Number of pages10
Publication statusPublished - 2007 Dec 1
Event8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007 - Birmingham, United Kingdom
Duration: 2007 Dec 162007 Dec 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4881 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007
CountryUnited Kingdom
CityBirmingham
Period07/12/1607/12/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Min, J. K., & Cho, S. B. (2007). Multiple classifier fusion using k-nearest localized templates. In Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings (pp. 447-456). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4881 LNCS).