International Journal of Man-Machine Studies
The Use of Background Knowledge in Decision Tree Induction
Machine Learning
Machine Learning
Checking an expert systems knowledge base for consistency and completeness
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
MINI: a heuristic approach for logic minimization
IBM Journal of Research and Development
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This paper discusses the important issue of knowledge base comprehensibility and describes a technique for comprehensibility improvement. Comprehensibility is often measured by simplicity of concept description. Even in the simplest form, however, there will be a number of different DNF (Disjunctive Normal Form) descriptions possible to represent the same concept, and each of these will have a different degree of comprehensibility. In other words, simplification does not necessarily guarantee improved comprehensibility. In this paper, the authors introduce three new comprehensibility criteria, similarity, continuity, and conformity, for use with tabular knowledge bases. In addition, they propose an algorithm to convert a decision table with poor comprehensibility to one with high comprehensibility, while preserving logical equivalency. In experiments, the algorithm generated either the same or similar tables to those generated by humans.