Rule Set Quality Measures for Inductive Learning Algorithms

Author

Ralf H. Klinkenberg
Daniel C. St. Clair, Missouri University of Science and Technology

Abstract

Symbolic inductive learning systems that induce concept descriptions from examples are valuable tools in the task of knowledge acquisition for expert systems. Since inductive learning methods produce distinctive concept descriptions when given identical training data, questions arise as to the quality of the different rule sets produced. This work provides several techniques for comparing and analyzing rule sets. These techniques measure the accuracy, generalization, time and space complexity, and domain coverage of rule sets. Based on these metrics, the performance of the rule sets generated by four different inductive learning systems is compared on six real world data sets.

Recommended Citation

R. H. Klinkenberg and D. C. St. Clair, "Rule Set Quality Measures for Inductive Learning Algorithms," Intelligent Engineering Systems Through Artificial Neural Networks, vol. 6, pp. 161 - 168, 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