Fuzzy discriminant analysis with outlier detection by genetic algorithm

Chang Chun Lin*, An Pin Chen

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

20 Scopus citations

Abstract

This paper proposes a method for performing fuzzy multiple discriminant analysis on groups of crisp data and determining the membership function of each group by minimizing the classification error using a genetic algorithm. Euclidean distance is used to measure the similarity between data points and defining membership functions. A numerical example is provided for illustration. The numerical example indicates that the classification obtained by fuzzy discriminant analysis is more satisfactory than that obtained by crisp discriminant analysis and is less fuzzy than that obtained by fuzzy cluster analysis. Moreover, the proposed fuzzy discriminant analysis is also a good approach to identifying outliers, of which the degree of membership to each group is zero.

Original languageEnglish
Pages (from-to)877-888
Number of pages12
JournalComputers and Operations Research
Volume31
Issue number6
DOIs
StatePublished - 05 2004
Externally publishedYes

Keywords

  • Fuzzy discriminant analysis
  • Fuzzy sets
  • Genetic algorithms

Fingerprint

Dive into the research topics of 'Fuzzy discriminant analysis with outlier detection by genetic algorithm'. Together they form a unique fingerprint.

Cite this