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

  • Celia C. Bojarczuk
  • Heitor S. Lopes
  • Alex A. Freitas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2610)

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.

Keywords

Genetic Programming Classification Rule Disjunctive Normal Form Classification Experiment Innovative Application 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [Bojarczuk et al. 2000]
    C. C. Bojarczuk, H. S. Lopes, A. A. Freitas. Genetic programming for knowledge discovery in chest pain diagnosis. IEEE Engineering in Medicine and Biology magazine-special issue on data mining and knowledge discovery, 19(4), 38–44, July/Aug. 2000.Google Scholar
  2. [Dhar et al. 2000]
    V. Dhar, D. Chou and F. Provost. Discovering interesting patterns for investment decision making with GLOWER-a genetic learner overlaid with entropy reduction. Data Mining and Knowledge Discovery Journal 4 (2000), 251–280.zbMATHCrossRefGoogle Scholar
  3. [Freitas 2002]
    A. A. Freitas. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, 2002.Google Scholar
  4. [Hand 1997]
    D. J. Hand. Construction and Assessment of Classification Rules. Chichester: John Wiley & Sons, 1997.zbMATHGoogle Scholar
  5. [Kishore et al. 2000]
    J. K. Kishore, L. M. Patnaik, V. Mani and V. K. Agrawal. Application of genetic programming for multicategory pattern classification. IEEE Transactions on Evolutionary Computation 4(3) (2000), 242–258.CrossRefGoogle Scholar
  6. [Montana 1995]
    D. J. Montana. Strongly typed genetic programming. Evolutionary Computation 3 (1995), 199–230.CrossRefGoogle Scholar
  7. [Papagelis and Kalles 2001]
    A. Papagelis and D. Kalles. Breeding decision trees using evolutionary techniques. Proc. 18 th Int. Conf. on Machine Learning, 393–400. San Mateo: Morgan Kaufmann, 2001.Google Scholar
  8. [Quinlan 1993]
    J. R. Quinlan. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.Google Scholar
  9. [Witten and Frank 2000]
    I. H. Witten and E. Frank. Data Mining: practical machine learning tools and techniques with Java implementations. San Mateo: Morgan Kaufmann, 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Celia C. Bojarczuk
    • 1
  • Heitor S. Lopes
    • 2
  • Alex A. Freitas
    • 3
  1. 1.Departamento de EletrotecnicaCEFET-PRCuritibaBrazil
  2. 2.CPGEI, CEFET-PRCuritibaBrazil
  3. 3.Computing LaboratoryUniversity of KentCanterburyUK

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