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Generating Effective Classifiers with Supervised Learning of Genetic Programming

  • Been-Chian Chien
  • Jui-Hsiang Yang
  • nd Wen-Yang Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)

Abstract

A new approach of learning classifiers using genetic programming has been developed recently. Most of the previous researches generate classification rules to classify data. However, the generation of rules is time consuming and the recognition accuracy is limited. In this paper, an approach of learning classification functions by genetic programming is proposed for classification. Since a classification function deals with numerical attributes only, the proposed scheme first transforms the nominal data into numerical values by rough membership functions. Then, the learning technique of genetic programming is used to generate classification functions. For the purpose of improving the accuracy of classification, we proposed an adaptive interval fitness function. Combining the learned classification functions with training samples, an effective classification method is presented. Numbers of data sets selected from UCI Machine Learning repository are used to show the effectiveness of the proposed method and compare with other classifiers.

Keywords

Genetic Programming Classification Function Association Rule Mining Positive Instance Nominal Attribute 
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 2003

Authors and Affiliations

  • Been-Chian Chien
    • 1
  • Jui-Hsiang Yang
    • 1
  • nd Wen-Yang Lin
    • 2
  1. 1.Institute of Information EngineeringI-Shou UniversityKaohsiung CountyTaiwan, R.O.C.
  2. 2.Department of Information ManagementI-Shou UniversityKaohsiung CountyTaiwan, R.O.C.

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