Evolving rule induction algorithms with multi-objective grammar-based genetic programming

Pappa, Gisele L. and Freitas, Alex A. (2009) Evolving rule induction algorithms with multi-objective grammar-based genetic programming. Knowledge and Information Systems, 19 (3). pp. 283-309. ISSN 0219-1377. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Multi-objective optimization has played a major role in solving problems where two or more conflicting objectives need to be simultaneously optimized. This paper presents a Multi-Objective grammar-based genetic programming (MOGGP) system that automatically evolves complete rule induction algorithms, which in turn produce both accurate and compact rule models. The system was compared with a single objective GGP and three other rule induction algorithms. In total, 20 UCI data sets were used to generate and test generic rule induction algorithms, which can be now applied to any classification data set. Experiments showed that, in general, the proposed MOGGP finds rule induction algorithms with competitive predictive accuracies and more compact models than the algorithms it was compared with.

Item Type: Article
Uncontrolled keywords: data mining, classification, genetic programming, rule induction
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: 29 Mar 2010 12:15
Last Modified: 19 May 2014 15:53
Resource URI: https://kar.kent.ac.uk/id/eprint/24110 (The current URI for this page, for reference purposes)
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