TrueSkill-based pairwise coupling for multi-class classification

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

2 Citations (Scopus)


A multi-class classification problem can be solved efficiently via decomposition of the problem into multiple binary classification problems. As a way of such decomposition, we propose a novel pairwise coupling method based on the TrueSkill ranking system. Instead of aggregating all pairwise binary classification results for the final decision, the proposed method keeps track of the ranks of the classes during the successive binary classification procedure. Especially, selection of a binary classifier at a certain step is done in such a way that the multi-class classification decision using the binary classification results up to the step converges to the final one as quickly as possible. Thus, the number of binary classifications can be reduced, which in turn reduces the computational complexity of the whole classification system. Experimental results show that the complexity is reduced significantly for no or minor loss of classification performance.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
Number of pages8
EditionPART 2
Publication statusPublished - 2012
Event22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland
Duration: 2012 Sept 112012 Sept 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7553 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other22nd International Conference on Artificial Neural Networks, ICANN 2012

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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