Using discriminant analysis for multi-class classification: An experimental investigation

Tao Li, Shenghuo Zhu, Mitsunori Ogihara

Research output: Contribution to journalArticlepeer-review

191 Scopus citations


Many supervised machine learning tasks can be cast as multi-class classification problems. Support vector machines (SVMs) excel at binary classification problems, but the elegant theory behind large-margin hyperplane cannot be easily extended to their multi-class counterparts. On the other hand, it was shown that the decision hyperplanes for binary classification obtained by SVMs are equivalent to the solutions obtained by Fisher's linear discriminant on the set of support vectors. Discriminant analysis approaches are well known to learn discriminative feature transformations in the statistical pattern recognition literature and can be easily extend to multi-class cases. The use of discriminant analysis, however, has not been fully experimented in the data mining literature. In this paper, we explore the use of discriminant analysis for multi-class classification problems. We evaluate the performance of discriminant analysis on a large collection of benchmark datasets and investigate its usage in text categorization. Our experiments suggest that discriminant analysis provides a fast, efficient yet accurate alternative for general multi-class classification problems.

Original languageEnglish (US)
Pages (from-to)453-472
Number of pages20
JournalKnowledge and Information Systems
Issue number4
StatePublished - Nov 2006
Externally publishedYes


  • Discriminant analysis
  • Multi-class classification

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence


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