Articles

Validation of machine learning techniques: decision trees and finite training set

[+] Author Affiliations
Chiou Peng Lam

Murdoch University, School of Engineering, South Street, Murdoch 6150, Western Australia

Geoffrey A. W. West, Terry M. Caelli

Curtin University of Technology, Department of Computer Science, GPO Box U1987, Perth 6001, Western Australia

J. Electron. Imaging. 7(1), 94-103 (Jan 01, 1998). doi:10.1117/1.482630
History: Received Mar. 10, 1997; Revised July 5, 1997; Accepted Aug. 5, 1997
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Abstract

There has been some recent interest in using machine learning techniques as part of pattern recognition systems. However, little attention is typically given to the validity of the features and types of rules generated by these systems and how well they perform across a variety of features and patterns. We focus on such issues of validity and comparative performance using two different types of decision tree techniques. In addition, we introduce the notion of including legal perturbations of objects in the training set and show that the performance of the resulting classifiers was better than that those trained without such “legal” constructs in the data selection. © 1998 SPIE and IS&T.

© 1998 SPIE and IS&T

Citation

Chiou Peng Lam ; Geoffrey A. W. West and Terry M. Caelli
"Validation of machine learning techniques: decision trees and finite training set", J. Electron. Imaging. 7(1), 94-103 (Jan 01, 1998). ; http://dx.doi.org/10.1117/1.482630


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