Fuzzy discriminant analysis with outlier detection by genetic algorithm

Fuzzy discriminant analysis with outlier detection by genetic algorithm

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Article ID: iaor20043776
Country: United Kingdom
Volume: 31
Issue: 6
Start Page Number: 877
End Page Number: 888
Publication Date: May 2004
Journal: Computers and Operations Research
Authors: ,
Keywords: fuzzy sets
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.

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