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An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients

Bojarczuk, Celia C. and Lopes, Heitor S. and Freitas, Alex A. (2003) An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients. In: Ryan, Conor and Keijzer, Maarten and Poli, Riccardo and Soule, Terence and Tsang, Edward and Costa, Ernesto, eds. Genetic Programming 6th European Conference. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 11-21. ISBN 978-3-540-00971-9. E-ISBN 978-3-540-36599-0. (doi:10.1007/3-540-36599-0_2) (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) (KAR id:13992)

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.
Official URL:
http://dx.doi.org/10.1007/3-540-36599-0_2

Abstract

This paper proposes a 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 in a real-world medical data set. This data set represents a difficult classification problem, and a new preprocessing method was devised for mining the data.

Item Type: Book section
DOI/Identification number: 10.1007/3-540-36599-0_2
Additional information: Conference Information: 6th European Conference on Genetic Programming (EuroGP 2003)
Uncontrolled keywords: genetic programming, data mining, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:01 UTC
Last Modified: 05 Nov 2024 09:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/13992 (The current URI for this page, for reference purposes)

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