Applying genetic programming technique in classification trees

Authors:
Chan-Sheng Kuo;Tzung-Pei Hong;Chuen-Lung Chen
Affiliations:
National Chengchi University, Department of Management Information Systems, 116, Taipei, Taiwan, ROC;National University of Kaohsiung, Department of Electrical Engineering, 811, Kaohsiung, Taiwan, ROC;National Chengchi University, Department of Management Information Systems, 116, Taipei, Taiwan, ROC
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on intelligent systems for financial engineering and computational finance
Year:
2007

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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.