TS-models from evidential clustering

R.J. Almeida, U. Kaymak

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Abstract

We study how to derive a fuzzy rule-based classification model using the theoretical framework of belief functions. For this purpose we use the recently proposed Evidential c-means (ECM) to derive Takagi-Sugeno (TS) models solely from data. ECM allocates, for each object, a mass of belief to any subsets of possible clusters, which allows to gain a deeper insight in the data while being robust with respect to outliers. Some classification examples are discussed, which show the advantages and disadvantages of the proposed algorithm.
Original languageEnglish
Title of host publicationInformation Processing and Management of Uncertainty in Knowledge-Based Systems : Theory and Methods (13th International Conference, IPMU 2010 Dortmund, Germany, June 28- July 2, 2010, Proceedings, Part I)
EditorsE. Hüllermeier, R. Kruse, F. Hoffmann
Place of PublicationBerlin
PublisherSpringer
Pages228-237
ISBN (Print)978-3-642-14054-9
DOIs
Publication statusPublished - 2010
Event13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2010) - Dortmund, Germany
Duration: 28 Jun 20102 Jul 2010
Conference number: 13

Publication series

NameCommunications in Computer and Information Science
Volume80
ISSN (Print)1865-0929

Conference

Conference13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2010)
Abbreviated titleIPMU 2010
CountryGermany
CityDortmund
Period28/06/102/07/10

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