Fuzzy rule extraction from typicality and membership partitions

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

doi:10.1109/FUZZY.2008.4630638

Fuzzy rule extraction from typicality and membership partitions

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

Information Systems IE&IS

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

3 Citations (Scopus)

4 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 language	English
Title of host publication	Proceedings of the 2008 IEEE international conference on fuzzy systems
Place of Publication	Hong Kong
Publisher	Institute of Electrical and Electronics Engineers
Pages	1964-1970
ISBN (Print)	978-1-4244-1818-3
DOIs	https://doi.org/10.1109/FUZZY.2008.4630638
Publication status	Published - 2008
Event	2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008) - Hong Kong Convention and Exhibition Centre, Hong Kong, Hong Kong Duration: 1 Jun 2008 → 6 Jun 2008

Conference

Conference	2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008)
Abbreviated title	FUZZ-IEEE 2008
Country/Territory	Hong Kong
City	Hong Kong
Period	1/06/08 → 6/06/08
Other	Conference held at the 2008 IEEE World Congress on Computational Intelligence (WCCI 2008)

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/FUZZY.2008.4630638

Cite this

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title = "Fuzzy rule extraction from typicality and membership partitions",

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.",

author = "R.J. Almeida and U. Kaymak and {Costa Sousa, da}, J.M.",

year = "2008",

doi = "10.1109/FUZZY.2008.4630638",

language = "English",

isbn = "978-1-4244-1818-3",

pages = "1964--1970",

booktitle = "Proceedings of the 2008 IEEE international conference on fuzzy systems",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008), FUZZ-IEEE 2008 ; Conference date: 01-06-2008 Through 06-06-2008",

}

Almeida, RJ, Kaymak, U & Costa Sousa, da, JM 2008, Fuzzy rule extraction from typicality and membership partitions. in Proceedings of the 2008 IEEE international conference on fuzzy systems. Institute of Electrical and Electronics Engineers, Hong Kong, pp. 1964-1970, 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008), Hong Kong, Hong Kong, 1/06/08. https://doi.org/10.1109/FUZZY.2008.4630638

Fuzzy rule extraction from typicality and membership partitions. / Almeida, R.J.; Kaymak, U.; Costa Sousa, da, J.M.
Proceedings of the 2008 IEEE international conference on fuzzy systems. Hong Kong: Institute of Electrical and Electronics Engineers, 2008. p. 1964-1970.

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

TY - GEN

T1 - Fuzzy rule extraction from typicality and membership partitions

AU - Almeida, R.J.

AU - Kaymak, U.

AU - Costa Sousa, da, J.M.

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

U2 - 10.1109/FUZZY.2008.4630638

DO - 10.1109/FUZZY.2008.4630638

M3 - Conference contribution

SN - 978-1-4244-1818-3

SP - 1964

EP - 1970

BT - Proceedings of the 2008 IEEE international conference on fuzzy systems

PB - Institute of Electrical and Electronics Engineers

CY - Hong Kong

T2 - 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008)

Y2 - 1 June 2008 through 6 June 2008

ER -

Fuzzy rule extraction from typicality and membership partitions

Abstract

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UN SDGs

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