A general purpose computer aid to judgemental forecasting: Rationale and procedure
Decision Support Systems
Synthesizing knowledge: A cluster analysis approach using event covering
IEEE Transactions on Systems, Man and Cybernetics
Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
An event-covering method for effective probabilistic inference
Pattern Recognition
Current developments in expert systems
Proceedings of the Second Australian Conference on Applications of expert systems
Inductive knowledge acquisition: a case study
Proceedings of the Second Australian Conference on Applications of expert systems
Learning strategies and automated knowledge acquisition: an overview
Computational models of learning
Synthesizing Statistical Knowledge from Incomplete Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
APACS: a system for the automatic analysis and classification of conceptual patterns
Computational Intelligence
Can Machine Learning Offer Anything to Expert Systems?
Machine Learning
Machine Learning
OBSERVER: A Probabilistic Learning System for Ordered Events
Proceedings of the 4th International Conference on Pattern Recognition
Generating production rules from decision trees
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
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In this paper, an inductive knowledge acquisition method that can be used as an aid for building certain medical expert systems is described. Given a collection of subjects that are described in terms of one or more attributes and are preclassified into a number of known classes (such as disease classes or classes that require certain type of therapy), this method is capable of detecting inherent probabilistic patterns in the data. Classificatory knowledge is then synthesized based on the detected patterns and made explicit in the form of classification rules. Based on these rules, the class membership of a subject can then be determined. The method has been implemented and tested with both simulated and real-world data. It has also been compared to some existing learning systems. The results show that the proposed learning method performs better both in terms of computational efficiency and classification accuracy.