GEPCLASS: A Classification Rule Discovery Tool Using Gene Expression Programming

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


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.


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

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