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Discovering comprehensible classification rules with a genetic algorithm

Fidelis, M.V. and Lopes, Heitor S. and Freitas, Alex A. (2000) Discovering comprehensible classification rules with a genetic algorithm. In: Proceedings of the 2000 Congress on Evolutionary Computation. IEEE, pp. 805-810. ISBN 0-7803-6375-2. (doi:10.1109/CEC.2000.870381) (KAR id:22013)


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: Book section
DOI/Identification number: 10.1109/CEC.2000.870381
Uncontrolled keywords: genetic algorithms; data mining; biological cells; search methods; encoding; genetic mutations; medical diagnostic imaging; breast cancer; classification algorithms; performance evaluation
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 09 Sep 2009 13:48 UTC
Last Modified: 16 Nov 2021 10:00 UTC
Resource URI: (The current URI for this page, for reference purposes)

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