Pappa, G.L. and Freitas, A.A. (2006) Automatically Evolving Rule Induction Algorithms. In: Fuernkranz, Johannes and Scheffer, Tobias and Spiliopoulou, Myra, eds. Machine Learning: ECML 2006. Lecture Notes in Computer Science, 4212 . Springer Berlin / Heidelberg, Berlin, pp. 341-352. ISBN 978-3-540-45375-8.
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|
|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:||Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group|
|Depositing User:||Mark Wheadon|
|Date Deposited:||24 Nov 2008 18:03|
|Last Modified:||06 Sep 2011 01:32|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/14416 (The current URI for this page, for reference purposes)|
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