Multiple classifier fusion using k-nearest localized templates

Jun Ki Min; Sung Bae Cho

doi:10.1007/978-3-540-77226-2_46

Multiple classifier fusion using k-nearest localized templates

Jun Ki Min, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (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 language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings
Publisher	Springer Verlag
Pages	447-456
Number of pages	10
ISBN (Print)	9783540772255
DOIs	https://doi.org/10.1007/978-3-540-77226-2_46
Publication status	Published - 2007
Event	8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007 - Birmingham, United Kingdom Duration: 2007 Dec 16 → 2007 Dec 19

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4881 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007
Country/Territory	United Kingdom
City	Birmingham
Period	07/12/16 → 07/12/19

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-77226-2_46

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). Springer Verlag. https://doi.org/10.1007/978-3-540-77226-2_46

Min, Jun Ki ; Cho, Sung Bae. / Multiple classifier fusion using k-nearest localized templates. Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings. Springer Verlag, 2007. pp. 447-456 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Multiple classifier fusion using k-nearest localized templates",

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.",

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Min, JK & Cho, SB 2007, Multiple classifier fusion using k-nearest localized templates. in Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4881 LNCS, Springer Verlag, pp. 447-456, 8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007, Birmingham, United Kingdom, 07/12/16. https://doi.org/10.1007/978-3-540-77226-2_46

Multiple classifier fusion using k-nearest localized templates. / Min, Jun Ki; Cho, Sung Bae.
Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings. Springer Verlag, 2007. p. 447-456 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4881 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Min, Jun Ki

AU - Cho, Sung Bae

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N2 - 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.

AB - 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.

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DO - 10.1007/978-3-540-77226-2_46

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BT - Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings

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Min JK, Cho SB. Multiple classifier fusion using k-nearest localized templates. In Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings. Springer Verlag. 2007. p. 447-456. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-77226-2_46

Multiple classifier fusion using k-nearest localized templates

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