Fuzzy rule extraction from typicality and membership partitions

R.J. Almeida, U. Kaymak, J.M. Costa Sousa, da

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

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

This paper proposes extracting fuzzy rules from data using fuzzy possibilistic c-means and possibilistic fuzzy c-means algorithms, which provide more than one partition information: the typicality matrix and the membership matrix. Usually to extract fuzzy rules from data only one of the partition matrix is used, resulting in one rule per cluster. In our work we extract rules from both the membership partition matrix and the typicality matrix, resulting in deriving multiple rules for each cluster. These methods are applied to fuzzy modeling of four different classification problems: Iris, Wine, Wisconsin breast cancer and Altman data sets. The performance of the obtained models is compared and we consider the added value of the proposed approach in fuzzy modeling.
Original languageEnglish
Title of host publicationProceedings of the 2008 IEEE international conference on fuzzy systems
Place of PublicationHong Kong
PublisherInstitute of Electrical and Electronics Engineers
Pages1964-1970
ISBN (Print)978-1-4244-1818-3
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008) - Hong Kong Convention and Exhibition Centre, Hong Kong, Hong Kong
Duration: 1 Jun 20086 Jun 2008

Conference

Conference2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008)
Abbreviated titleFUZZ-IEEE 2008
CountryHong Kong
CityHong Kong
Period1/06/086/06/08
OtherConference held at the 2008 IEEE World Congress on Computational Intelligence (WCCI 2008)

Fingerprint Dive into the research topics of 'Fuzzy rule extraction from typicality and membership partitions'. Together they form a unique fingerprint.

Cite this