Maximum likelihood parameter estimation in probabilistic fuzzy classifiers

L.R. Waltman, U. Kaymak, J. Berg, van den

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

25 Citations (Scopus)

Abstract

Probabilistic fuzzy systems make it possible to model linguistic uncertainty and probabilistic uncertainty in a single system. This paper is concerned with the estimation of the parameters in probabilistic fuzzy classifiers. The purpose of the paper is to introduce a new method that simultaneously estimates all the parameters in a probabilistic fuzzy classifier. The method uses a maximum likelihood criterion and a gradient-based optimization algorithm. The performance of the method is evaluated on two benchmark data sets. The method is compared with a sequential parameter estimation method used in previous publications. Also, a comparison with an alternative method from the literature is made.
Original languageEnglish
Title of host publicationThe 14th IEEE International Conference on Fuzzy Systems (FUZZ '05), 25 May 2005, Reno
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1098-1103
ISBN (Print)0-7803-9159-4
DOIs
Publication statusPublished - 2005

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