Discovering comprehensible classification rules with a genetic algorithm

Fidelis, M.V. and Lopes, H.S. and Freitas, A.A. (2000) Discovering comprehensible classification rules with a genetic algorithm. In: Evolutionary Computation, 2000. Proceedings of the 2000 Congress on. IEEE, La Jolla, CA, USA pp. 805-810. ISBN 0-7803-6375-2. (Full text available)

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http://dx.doi.org/10.1109/CEC.2000.870381

Abstract

Presents a classification algorithm based on genetic algorithms (GAs) that discovers comprehensible IF-THEN rules, in the spirit of data mining. The proposed GA has a flexible chromosome encoding, where each chromosome corresponds to a classification rule. Although the number of genes (the genotype) is fixed, the number of rule conditions (the phenotype) is variable. The GA also has specific mutation operators for this chromosome encoding. The algorithm was evaluated on two public-domain real-world data sets (in the medical domains of dermatology and breast cancer)

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 09 Sep 2009 13:48
Last Modified: 29 May 2012 09:56
Resource URI: http://kar.kent.ac.uk/id/eprint/22013 (The current URI for this page, for reference purposes)
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