Binary classifier fusion based on the basic decomposition methods

Jaepil Ko; Hyeran Byun

doi:10.1007/3-540-44938-8_15

Binary classifier fusion based on the basic decomposition methods

Jaepil Ko, Hyeran Byun

College of Computing

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

8 Citations (Scopus)

Abstract

For a complex multiclass problem, it is common to construct the multiclass classifier by combining the outputs of several binary ones. The two basic methods for this purpose are known as one-per-class (OPC) and pairwise coupling (PWC) and their general form is error correcting output code (ECOC). In this paper, we review basic decomposition methods and introduce a new sequential fusion method based on OPC and PWC according to their properties. In the experiments, we compare our proposed method with each basic method and ECOC method. The experimental results show that our proposed method can improve significantly the classification accuracy on the real dataset.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Terry Windeatt, Fabio Roli
Publisher	Springer Verlag
Pages	146-155
Number of pages	10
ISBN (Print)	3540403698, 9783540403692
DOIs	https://doi.org/10.1007/3-540-44938-8_15
Publication status	Published - 2003

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2709
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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10.1007/3-540-44938-8_15

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Ko, J., & Byun, H. (2003). Binary classifier fusion based on the basic decomposition methods. In T. Windeatt, & F. Roli (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 146-155). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2709). Springer Verlag. https://doi.org/10.1007/3-540-44938-8_15

Ko, Jaepil ; Byun, Hyeran. / Binary classifier fusion based on the basic decomposition methods. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Terry Windeatt ; Fabio Roli. Springer Verlag, 2003. pp. 146-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Ko, J & Byun, H 2003, Binary classifier fusion based on the basic decomposition methods. in T Windeatt & F Roli (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2709, Springer Verlag, pp. 146-155. https://doi.org/10.1007/3-540-44938-8_15

Binary classifier fusion based on the basic decomposition methods. / Ko, Jaepil; Byun, Hyeran.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Terry Windeatt; Fabio Roli. Springer Verlag, 2003. p. 146-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2709).

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

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AB - For a complex multiclass problem, it is common to construct the multiclass classifier by combining the outputs of several binary ones. The two basic methods for this purpose are known as one-per-class (OPC) and pairwise coupling (PWC) and their general form is error correcting output code (ECOC). In this paper, we review basic decomposition methods and introduce a new sequential fusion method based on OPC and PWC according to their properties. In the experiments, we compare our proposed method with each basic method and ECOC method. The experimental results show that our proposed method can improve significantly the classification accuracy on the real dataset.

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Ko J, Byun H. Binary classifier fusion based on the basic decomposition methods. In Windeatt T, Roli F, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2003. p. 146-155. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-44938-8_15

Binary classifier fusion based on the basic decomposition methods

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