TS-Models from Evidential Clustering

Rui Almeida e Santos Nogueira; U Kaymak

doi:10.1007/978-3-642-14055-6_24

TS-Models from Evidential Clustering

Rui Almeida e Santos Nogueira, U Kaymak

Research output: Chapter/Conference proceeding › Chapter › Academic

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 language	English
Title of host publication	Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. - Part I
Editors	R. Kruse, F. Hoffmann, E. Hüllermeier
Place of Publication	Berlin
Publisher	Springer-Verlag
Pages	228-237
Number of pages	760
ISBN (Print)	9783642140549
DOIs	https://doi.org/10.1007/978-3-642-14055-6_24
Publication status	Published - 2010

Publication series

Series	Communications in Computer and Information Science
Volume	80

Research programs

EUR ESE 31
EUR ESE 32

Access to Document

10.1007/978-3-642-14055-6_24

Cite this

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title = "TS-Models from Evidential Clustering",

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.",

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Almeida e Santos Nogueira, R & Kaymak, U 2010, TS-Models from Evidential Clustering. in R Kruse, F Hoffmann & E Hüllermeier (eds), Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. - Part I. Springer-Verlag, Berlin, Communications in Computer and Information Science, vol. 80, pp. 228-237. https://doi.org/10.1007/978-3-642-14055-6_24

TS-Models from Evidential Clustering. / Almeida e Santos Nogueira, Rui; Kaymak, U.
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. - Part I. ed. / R. Kruse; F. Hoffmann; E. Hüllermeier. Berlin: Springer-Verlag, 2010. p. 228-237 (Communications in Computer and Information Science, Vol. 80).

Research output: Chapter/Conference proceeding › Chapter › Academic

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AU - Kaymak, U

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N2 - 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.

AB - 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.

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BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. - Part I

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Almeida e Santos Nogueira R, Kaymak U. TS-Models from Evidential Clustering. In Kruse R, Hoffmann F, Hüllermeier E, editors, Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. - Part I. Berlin: Springer-Verlag. 2010. p. 228-237. (Communications in Computer and Information Science, Vol. 80). doi: 10.1007/978-3-642-14055-6_24

TS-Models from Evidential Clustering

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