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Applying genetic programming technique in classification trees

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Abstract

Classification problems are often encountered in many applications. In the past, classification trees were often generated by decision-tree methods and commonly used to solve classification problems. In this paper, we have proposed an algorithm based on genetic programming to search for an appropriate classification tree according to some criteria. The classification tree obtained can be transferred into a rule set, which can then be fed into a knowledge base to support decision making and facilitate daily operations. Two new genetic operators, elimination and merge, are designed in the proposed approach to remove redundancy and subsumption, thus producing more accurate and concise decision rules than that without using them. Experimental results from the credit card data also show the feasibility of the proposed algorithm.

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Authors and Affiliations

  1. Department of Management Information Systems, National Chengchi University, 116, Taipei, Taiwan, ROC

    Chan-Sheng Kuo & Chuen-Lung Chen

  2. Department of Electrical Engineering, National University of Kaohsiung, 811, Kaohsiung, Taiwan, ROC

    Tzung-Pei Hong

Authors
  1. Chan-Sheng Kuo
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  2. Tzung-Pei Hong
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  3. Chuen-Lung Chen
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Correspondence to Chan-Sheng Kuo.

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Kuo, CS., Hong, TP. & Chen, CL. Applying genetic programming technique in classification trees. Soft Comput 11, 1165–1172 (2007). https://doi.org/10.1007/s00500-007-0159-x

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  • DOI: https://doi.org/10.1007/s00500-007-0159-x

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