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Automatically Evolving Rule Induction Algorithms

Pappa, Gisele L. and Freitas, Alex A. (2006) Automatically Evolving Rule Induction Algorithms. In: Fuernkranz, Johannes and Scheffer, Tobias and Spiliopoulou, Myra, eds. Machine Learning: ECML 2006 17th European Conference on Machine Learning. Lecture Notes in Computer Science . Springer, Berlin, pp. 341-352. ISBN 978-3-540-45375-8. E-ISBN 978-3-540-46056-5. (doi:10.1007/11871842_34) (KAR id:14416)


Research in the rule induction algorithm field produced many algorithms in the last 30 years. However, these algorithms are usually obtained from a few basic rule induction algorithms that have been often changed to produce better ones. Having these basic algorithms and their components in mind, this work proposes the use of Grammar-based Genetic Programming (GGP) to automatically evolve rule induction algorithms. The proposed GGP is evaluated in extensive computational experiments involving 11 data sets. Overall, the results show that effective rule induction algorithms can be automatically generated using GGP. The automatically evolved rule induction algorithms were shown to be competitive with well-known manually designed ones. The proposed approach of automatically evolving rule induction algorithms can be considered a pioneering one, opening a new kind of research area.

Item Type: Book section
DOI/Identification number: 10.1007/11871842_34
Uncontrolled keywords: rule induction algorithms, data mining, grammar-based genetic programming
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: 24 Nov 2008 18:03 UTC
Last Modified: 16 Nov 2021 09:52 UTC
Resource URI: (The current URI for this page, for reference purposes)

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