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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007
Pages550-555
Number of pages6
DOIs
StatePublished - 27 Sep 2007
Event2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007 - Honolulu, HI, United States
Duration: 1 Apr 20075 Apr 2007

Publication series

NameProceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007

Conference

Conference2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007
CountryUnited States
CityHonolulu, HI
Period1/04/075/04/07

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