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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.
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 d¡¦Ombriano, Crema, Italy.