Distance Guided Classification with Gene Expression Programming

  • Lei Duan
  • Changjie Tang
  • Tianqing Zhang
  • Dagang Wei
  • Huan Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Gene Expression Programming (GEP) aims at discovering essential rules hidden in observed data and expressing them mathematically. GEP has been proved to be a powerful tool for constructing efficient classifiers. Traditional GEP-classifiers ignore the distribution of samples, and hence decrease the efficiency and accuracy. The contributions of this paper include: (1) proposing two strategies of generating classification threshold dynamically, (2) designing a new approach called Distance Guided Evolution Algorithm (DGEA) to improve the efficiency of GEP, and (3) demonstrating the effectiveness of generating classification threshold dynamically and DGEA by extensive experiments. The results show that the new methods decrease the number of evolutional generations by 83% to 90%, and increase the accuracy by 20% compared with the traditional approach.


Gene Expression Programming Classification Rule Flea Beetle Sequential Minimal Optimization Classification Threshold 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lei Duan
    • 1
  • Changjie Tang
    • 1
  • Tianqing Zhang
    • 1
  • Dagang Wei
    • 1
  • Huan Zhang
    • 1
  1. 1.School of Computer ScienceSichuan UniversityChengduChina

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