Advertisement

GEPCLASS: A Classification Rule Discovery Tool Using Gene Expression Programming

  • Wagner R. Weinert
  • Heitor S. Lopes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

This work describes the use of a recently proposed technique – gene expression programming – for knowledge discovery in the data mining task of data classification. We propose a new method for rule encoding and genetic operators that preserve rule integrity, and implemented a system, named GEPCLASS. Due to its encoding scheme, the system allows the automatic discovery of flexible rules, better fitted to data. The performance of GEPCLASS was compared with two genetic programming systems and with C4.5, over four data sets in a five-fold cross-validation procedure. The predictive accuracy for the methods compared were similar, but the computational effort needed by GEPCLASS was significantly smaller than the other. GEPCLASS was able to find simple and accurate rules as it can handle continuous and categorical attributes.

Keywords

Encode Scheme Genetic Operator Gene Expression Programming Closure Property Data Mining Task 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bojarczuk, C.C., Lopes, H.S., Freitas, A.A.: Genetic programming for knowledge discovery in chest pain diagnosis. IEEE Eng. Med. Biol. 19, 38–44 (2000)CrossRefGoogle Scholar
  2. 2.
    Bojarczuk, C.C., Lopes, H.S., Freitas, A.A.: A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets. Artif. Intell. Med. 30, 27–48 (2004)CrossRefGoogle Scholar
  3. 3.
    Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13, 87–129 (2001)zbMATHGoogle Scholar
  4. 4.
    Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, Berlin (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Hand, D.: Construction and Assessment of Classification Rules. John-Wiley & Sons, New-York (1997)zbMATHGoogle Scholar
  6. 6.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  7. 7.
    Lopes, H.S., Coutinho, M.S., Lima, W.C.: An evolutionary approach to simulate cognitive feedback learning in medical domain. In: Sanchez, E., et al. (eds.) Genetic Algorithms and Fuzzy Logic Systems, pp. 193–207. World Scientific, Singapore (1997)CrossRefGoogle Scholar
  8. 8.
    Lopes, H.S., Weinert, W.R.: EGIPSYS: an enhanced gene expression programming approach for symbolic regression problems. Int. J. Appl. Math. Comput. Sci. 14(3), 375–384 (2004)zbMATHMathSciNetGoogle Scholar
  9. 9.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kauffmann, San Mateo (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wagner R. Weinert
    • 1
  • Heitor S. Lopes
    • 1
  1. 1.Bioinformatics Laboratory, CPGEIFederal University of Technology – ParanáCuritiba (PR)Brazil

Personalised recommendations