A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets

Bojarczuk, C.C. and Lopes, H.S. and Freitas, A.A. and Michalkiewicz, E.L. (2004) A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets. Artificial Intelligence in Medicine, 30 (1). pp. 27-48. ISSN 0933-3657. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1016/j.artmed.2003.06.001

Abstract

This paper proposes a new constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets. The proposed GP contains several syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret. The GP is compared with C4.5, a well-known decision-tree-building algorithm, and with another GP that uses Boolean inputs (BGP), in five medical data sets: chest pain, Ljubljana breast cancer, dermatology, Wisconsin breast cancer, and pediatric adrenocortical tumor. For this last data set a new preprocessing step was devised for survival prediction. Computational experiments show that, overall, the GP algorithm obtained good results with respect to predictive accuracy and rule comprehensibility, by comparison with C4.5 and BGP.

Item Type: Article
Uncontrolled keywords: data mining, evolutionary algorithms, genetic programming, classification, medical data set
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 18:02
Last Modified: 08 May 2012 13:49
Resource URI: http://kar.kent.ac.uk/id/eprint/14220 (The current URI for this page, for reference purposes)
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