International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
An Iterative Growing and Pruning Algorithm for Classification Tree Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
A composite approach to inducing knowledge for expert systems design
Management Science
Machine Learning
Comparative Performance of Rule Quality Measures in an InductionSystem
Applied Intelligence
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Elicitation of Knowledge from Multiple Experts Using Network Inference
IEEE Transactions on Knowledge and Data Engineering
Proposition of the quality measure for the probabilistic decision support system
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Application of the confidence measure in knowledge acquisition process
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
Knowledge source confidence measure applied to a rule-based recognition system
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Combining classifiers under probabilistic models: experimental comparative analysis of methods
Expert Systems: The Journal of Knowledge Engineering
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Organizational databases are being used to develop rules or guidelines for action that are incorporated into decision processes. Tree induction algorithms of two types, total branching and subset elimination, used in the generation of rules, are reviewed with respect to their treatment of the issue of quality. Based on this assessment, a hybrid approach, probabilistic inductive learning (PrIL), is presented. It provides a probabilistic measure of goodness for an individual rule, enabling the user to set maximum misclassification levels, or minimum reliability levels, with predetermined confidence that each and every rule will satisfy this criterion. The user is able to quantify the reliability of the decision process, i.e., the invoking of the rules, which is of crucial importance in automated decision processes. PrIL and its associated algorithm are described. An illustrative example based on the claims process at a workers' compensation board is presented.