Abstract
The ability to learn has long been an area of interest to researchers in artificial intelligence. Symbolic inductive learning algorithms have evolved as a class of algorithms that can be used to learn concepts from training examples. The knowledge acquired is represented in the form of rules. Since symbolic learning methods develop distinctive sets of rules when given identical training data, questions arise as to the quality of the different rule sets produced. The results of this research provide techniques for comparing and analyzing rule sets. Numerous rule sets were generated using three well-known symbolic learning methods; Quinlan's ID3, Clark and Niblett's CN2, and Murray's Multiple Convergence algorithm. The analysis techniques were then applied to evaluate these sets of rules. The techniques as well as a guide for using them are presented in a concise summary following the discussion of the experimental results.
Recommended Citation
E. M. Boll and D. C. St. Clair, "Analysis of Rule Sets Generated by the CN2, ID3, and Multiple Convergence Symbolic Learning Methods," Proceedings - ACM Computer Science Conference, pp. 48 - 55, Association for Computing Machinery, Jan 1995.
The definitive version is available at https://doi.org/10.1145/259526.259537
Department(s)
Mathematics and Statistics
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
01 Jan 1995
