Pappa, G.L. and Freitas, A.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.
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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.
|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:||29 Mar 2010 12:15|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/24110 (The current URI for this page, for reference purposes)|
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