N-ary decomposition for multi-class classification

Joey Tianyi Zhou, Ivor W. Tsang, Shen Shyang Ho, Klaus Muller

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

A common way of solving a multi-class classification problem is to decompose it into a collection of simpler two-class problems. One major disadvantage is that with such a binary decomposition scheme it may be difficult to represent subtle between-class differences in many-class classification problems due to limited choices of binary-value partitions. To overcome this challenge, we propose a new decomposition method called N-ary decomposition that decomposes the original multi-class problem into a set of simpler multi-class subproblems. We theoretically show that the proposed N-ary decomposition could be unified into the framework of error correcting output codes and give the generalization error bound of an N-ary decomposition for multi-class classification. Extensive experimental results demonstrate the state-of-the-art performance of our approach.

Original languageEnglish
Pages (from-to)809-830
Number of pages22
JournalMachine Learning
Volume108
Issue number5
DOIs
Publication statusPublished - 2019 May 15

Keywords

  • Ensemble learning
  • Multi-class classification
  • N-ary ECOC

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Zhou, J. T., Tsang, I. W., Ho, S. S., & Muller, K. (2019). N-ary decomposition for multi-class classification. Machine Learning, 108(5), 809-830. https://doi.org/10.1007/s10994-019-05786-2