Using Grouping and Uncertain Reasoning during ID3 Decision Tree Construction and Testing

Author

Carmela R.C. Santos
Peter E. Maher
Daniel C. St. Clair, Missouri University of Science and Technology

Abstract

Quinlan's ID3 algorithm produces classification trees that perform poorly when used with noisy or continuous-valued data. The UR-ID3 algorithm combines uncertain reasoning with the IDS rule set to handle approximate test data. This paper presents a variation, called UR-ID3e, that assumes that training data is also uncertain. Experimental results are presented that compare the performance of UR-ID3e with other algorithms.

Recommended Citation

C. R. Santos et al., "Using Grouping and Uncertain Reasoning during ID3 Decision Tree Construction and Testing," Intelligent Engineering Systems Through Artificial Neural Networks, vol. 6, pp. 1093 - 1098, Dec 1996.

Department(s)

Mathematics and Statistics

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 The Authors, All rights reserved.

Publication Date

01 Dec 1996