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Journal of Information Science and Engineering, Vol. 22 No. 3, pp. 573-594 (May 2006)

A Classification Tree Based on Discriminant Functions*

Been-Chian Chien, Jung-Yi Lin1 and Wei-Pang Yang1,2
Department of Computer Science and Information Engineering
National University of Tainan
Tainan, 700 Taiwan
1Department of Computer and Information Science
National Chiao Tung University
Hsinchu, 300 Taiwan
2Department of Information Management
National Dong Hwa University
Hualien, 974 Taiwan

The classification problem is an important topic in knowledge discovery and machine learning. Traditional classification tree methods and their improvements have been discussed widely. This work proposes a new approach to construct decision trees based on discriminant functions which are learned using genetic programming. A discriminant function is a mathematical function for classifying data into a specific class. To learn discriminant functions effectively and efficiently, a distance-based fitness function for genetic programming is designed. After the set of discriminant functions for all classes is generated, a classifier is created as a binary decision tree with the Z-value measure to resolve the problem of ambiguity among discriminant functions. Several popular datasets from the UCI Repository were selected to illustrate the effectiveness of the proposed classifiers by comparing with previous methods. The results show that the proposed classification tree demonstrates high accuracy on the selected datasets.

Keywords: knowledge discovery, machine learning, genetic programming, classification, discriminant function, decision tree, classifier

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Received February 12, 2004; revised June 18 & November 5, 2004 & February 21 & April 7, 2005; accepted August 17, 2005.
Communicated by Chuen-Tsai Sun.
* Part of this paper was presented at the Sixth International Conference on Knowledge-Based Intelligent Information Engineering Systems,16-18 September, 2002, Podere díŽOmbriano, Crema, Italy.