A comparison of different fitness functions for extracting membership functions used in fuzzy data mining

Chun Hao Chen; Tzung Pei Hong; S. Tseng

doi:10.1109/FOCI.2007.371526

A comparison of different fitness functions for extracting membership functions used in fuzzy data mining

Chun Hao Chen^*, Tzung Pei Hong, S. Tseng

^*Corresponding author for this work

Institute of Data Science and Engineering

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

5 Scopus citations

Abstract

In this paper, a GA-based framework for finding membership functions suitable for fuzzy mining problems is proposed. Each individual represents a possible set of membership functions for the items and is divided into two parts, control genes and parametric genes. Control genes are encoded into binary strings and used to determine whether membership functions are active or not. Each set of membership functions for an item is encoded as parametric genes with real-number schema. Seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. Experiments are also made to show the effectiveness of the framework and to compare the seven fitness functions.

Original language	English
Title of host publication	Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007
Pages	550-555
Number of pages	6
DOIs	https://doi.org/10.1109/FOCI.2007.371526
State	Published - 27 Sep 2007
Event	2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007 - Honolulu, HI, United States Duration: 1 Apr 2007 → 5 Apr 2007

Publication series

Name	Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007

Conference

Conference	2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007
Country	United States
City	Honolulu, HI
Period	1/04/07 → 5/04/07

Access to Document

10.1109/FOCI.2007.371526

Link to publication in Scopus

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Chen, C. H., Hong, T. P., & Tseng, S. (2007). A comparison of different fitness functions for extracting membership functions used in fuzzy data mining. In Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007 (pp. 550-555). [4233960] (Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007). https://doi.org/10.1109/FOCI.2007.371526

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title = "A comparison of different fitness functions for extracting membership functions used in fuzzy data mining",

abstract = "In this paper, a GA-based framework for finding membership functions suitable for fuzzy mining problems is proposed. Each individual represents a possible set of membership functions for the items and is divided into two parts, control genes and parametric genes. Control genes are encoded into binary strings and used to determine whether membership functions are active or not. Each set of membership functions for an item is encoded as parametric genes with real-number schema. Seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. Experiments are also made to show the effectiveness of the framework and to compare the seven fitness functions.",

author = "Chen, {Chun Hao} and Hong, {Tzung Pei} and S. Tseng",

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note = "null ; Conference date: 01-04-2007 Through 05-04-2007",

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Chen, CH, Hong, TP & Tseng, S 2007, A comparison of different fitness functions for extracting membership functions used in fuzzy data mining. in Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007., 4233960, Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007, pp. 550-555, 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007, Honolulu, HI, United States, 1/04/07. https://doi.org/10.1109/FOCI.2007.371526

A comparison of different fitness functions for extracting membership functions used in fuzzy data mining. / Chen, Chun Hao; Hong, Tzung Pei; Tseng, S.

Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007. 2007. p. 550-555 4233960 (Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007).

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

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AB - In this paper, a GA-based framework for finding membership functions suitable for fuzzy mining problems is proposed. Each individual represents a possible set of membership functions for the items and is divided into two parts, control genes and parametric genes. Control genes are encoded into binary strings and used to determine whether membership functions are active or not. Each set of membership functions for an item is encoded as parametric genes with real-number schema. Seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. Experiments are also made to show the effectiveness of the framework and to compare the seven fitness functions.

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Chen CH, Hong TP, Tseng S. A comparison of different fitness functions for extracting membership functions used in fuzzy data mining. In Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007. 2007. p. 550-555. 4233960. (Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007). https://doi.org/10.1109/FOCI.2007.371526