Abstract
ID3 is most successful when used with sets of training and testing data that contain no missing attribute values. Many times, however, real-world domains have attributes with missing values. Sometimes these attribute values may not be needed to classify an instance. Such attribute values are called don't-care attribute values. In other cases, the values are needed but are unavailable. These values are called unknown attribute values. This paper describes the difference between unknown and don't-care attribute values and discusses several ways of identifying don't-care attribute values in ID3. Numerical results are described which validate the practicality of these approaches.
Recommended Citation
Dorr, P. D. and St. Clair, D. C., "The Identification and Processing of Don't-Care Attribute Values in ID3 Decision Tree Construction" (1992). Computer Science Technical Reports. 138.
https://scholarsmine.mst.edu/comsci_techreports/138
Department(s)
Computer Science
Second Department
Mathematics and Statistics
Keywords and Phrases
Automated Induction, Machine Learning, Knowledge Representation
Report Number
CSc-92-09
Document Type
Technical Report
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 1992 University of Missouri - Rolla, All rights reserved
Publication Date
1 May, 1992
Comments
The first Author is a Graduate Student
This report is substantially the M.S. thesis of the first author, completed May, 1992.
This thesis has been prepared in the style utilized by the Association for Computing Machinery (ACM). Pages 1-52 will be presented for publication in the journal Communications of the ACM. Appendices A, B, and C have been added for purposes normal to thesis writing.