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An Innovative Application of a Constrained-Syntax Genetic Programming System to the Problem of Predicting Survival of Patients

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Book cover Genetic Programming (EuroGP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2610))

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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.

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References

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Author information

Authors and Affiliations

  1. Departamento de Eletrotecnica, CEFET-PR, Av. 7 de setembro, 3165, 80230-901, Curitiba, Brazil

    Celia C. Bojarczuk

  2. CPGEI, CEFET-PR, Av. 7 de setembro, 3165, 80230-901, Curitiba, Brazil

    Heitor S. Lopes

  3. Computing Laboratory, University of Kent, CT2 7NF, Canterbury, UK

    Alex A. Freitas

Authors
  1. Celia C. Bojarczuk
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  2. Heitor S. Lopes
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  3. Alex A. Freitas
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Editor information

Editors and Affiliations

  1. Computer Science and Information Systems, University of Limerick, Limerick, Ireland

    Conor Ryan

  2. Department of Computer Science, University of Idaho, 83844-1010, Moscow, ID, USA

    Terence Soule

  3. Department of Computer Science, Free University of Amsterdam, 1081, HV Amsterdam, The Netherlands

    Maarten Keijzer

  4. Department of Computer Science, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Edward Tsang  & Riccardo Poli  & 

  5. Department of Computer Science, University of Coimbra, Polo II, Pinhal Marrocos, 3030-290, Coimbra, Portugal

    Ernesto Costa

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© 2003 Springer-Verlag Berlin Heidelberg

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Bojarczuk, C.C., Lopes, H.S., Freitas, A.A. (2003). An Innovative Application of a Constrained-Syntax Genetic Programming System to the Problem of Predicting Survival of Patients. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_2

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  • DOI: https://doi.org/10.1007/3-540-36599-0_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00971-9

  • Online ISBN: 978-3-540-36599-0

  • eBook Packages: Springer Book Archive

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