Binary classifier fusion based on the basic decomposition methods

Jaepil Ko, Hyeran Byun

Research output: Chapter in Book/Report/Conference proceedingChapter

6 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 languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsTerry Windeatt, Fabio Roli
PublisherSpringer Verlag
Pages146-155
Number of pages10
ISBN (Print)3540403698, 9783540403692
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2709
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    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