TS-models from evidential clustering

R.J. Almeida; U. Kaymak

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

TS-models from evidential clustering

R.J. Almeida, U. Kaymak

Information Systems IE&IS

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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 language	English
Title of host publication	Information 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)
Editors	E. Hüllermeier, R. Kruse, F. Hoffmann
Place of Publication	Berlin
Publisher	Springer
Pages	228-237
ISBN (Print)	978-3-642-14054-9
DOIs	https://doi.org/10.1007/978-3-642-14055-6_24
Publication status	Published - 2010
Event	13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2010) - Dortmund, Germany Duration: 28 Jun 2010 → 2 Jul 2010 Conference number: 13

Publication series

Name	Communications in Computer and Information Science
Volume	80
ISSN (Print)	1865-0929

Conference

Conference	13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2010)
Abbreviated title	IPMU 2010
Country/Territory	Germany
City	Dortmund
Period	28/06/10 → 2/07/10

Access to Document

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

Metis246583Final published version, 216 KB

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Almeida, R. J., & Kaymak, U. (2010). TS-models from evidential clustering. In E. Hüllermeier, R. Kruse, & F. Hoffmann (Eds.), Information 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) (pp. 228-237). (Communications in Computer and Information Science; Vol. 80). Springer. https://doi.org/10.1007/978-3-642-14055-6_24

Almeida, R.J. ; Kaymak, U. / TS-models from evidential clustering. Information 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). editor / E. Hüllermeier ; R. Kruse ; F. Hoffmann. Berlin : Springer, 2010. pp. 228-237 (Communications in Computer and Information Science).

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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.",

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Almeida, RJ & Kaymak, U 2010, TS-models from evidential clustering. in E Hüllermeier, R Kruse & F Hoffmann (eds), Information 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). Communications in Computer and Information Science, vol. 80, Springer, Berlin, pp. 228-237, 13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2010), Dortmund, Germany, 28/06/10. https://doi.org/10.1007/978-3-642-14055-6_24

TS-models from evidential clustering. / Almeida, R.J.; Kaymak, U.
Information 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). ed. / E. Hüllermeier; R. Kruse; F. Hoffmann. Berlin: Springer, 2010. p. 228-237 (Communications in Computer and Information Science; Vol. 80).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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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Almeida RJ, Kaymak U. TS-models from evidential clustering. In Hüllermeier E, Kruse R, Hoffmann F, editors, Information 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). Berlin: Springer. 2010. p. 228-237. (Communications in Computer and Information Science). doi: 10.1007/978-3-642-14055-6_24

TS-models from evidential clustering

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