Pappa, G.L. and Freitas, A.A. (2007) Discovering new rule induction algorithms with grammar-based genetic programming. In: Maimon, O. and Rokach, L., eds. Soft Computing for Knowledge Discovery and Data Mining. Springer, New York, pp. 133-152. ISBN 9780387699349.
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Rule induction is a data mining technique used to extract classification rules of the form IF (conditions) THEN (predicted class) from data. The majority of the rule induction algorithms found in the literature follow the sequential covering strategy, which essentially induces one rule at a time until (almost) all the training data is covered by the induced rule set. This strategy describes a basic algorithm composed by several key elements, which can be modified and/or extended to generate new and better rule induction algorithms. With this in mind, this work proposes the use of a grammar-based genetic programming (GGP) algorithm to automatically discover new sequential covering algorithms. The proposed system is evaluated using 20 data sets, and the automatically-discovered rule induction algorithms are compared with four well-known human-designed rule induction algorithms. Results showed that the GGP system is a promising approach to effectively discover new sequential covering algorithms.
|Item Type:||Book section|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Computing|
|Depositing User:||Suzanne Duffy|
|Date Deposited:||03 Oct 2008 13:45|
|Last Modified:||27 Jun 2009 11:14|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/12968 (The current URI for this page, for reference purposes)|
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