Fuzzy canonical discriminant analysis: Theory and practice

Ben Chang Shia, Jianping Zhu, Kuangnan Fang, Shuangge Ma

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this article, we propose a new classification method called fuzzy canonical discriminant analysis (FCDA) based on the Fisher's canonical discriminant analysis (CDA) to deal with some vagueness in natural and social science and to improve its prediction accuracy. By establishing the fuzzy canonical discriminant function and triangular function transformation, we obtain the estimators of parameters. We also design an efficient algorithm for calculation of the parameters. We compare it with CDA using the original Iris data, samples of the Iris data, and seven other popular data sets. The results confirms that the FCDA is an effective tool in prediction and is better than the CDA.

Original languageEnglish
Pages (from-to)1526-1539
Number of pages14
JournalCommunications in Statistics: Simulation and Computation
Volume40
Issue number10
DOIs
Publication statusPublished - Nov 2011
Externally publishedYes

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Keywords

  • Classification
  • Fuzzy canonical discriminant analysis
  • Fuzzy set theory

ASJC Scopus subject areas

  • Modelling and Simulation
  • Statistics and Probability

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