Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Using concept learning for knowledge acquisition
International Journal of Man-Machine Studies
Performance analysis of a probabilistic inductive learning system
Proceedings of the seventh international conference (1990) on Machine learning
Machine learning: an artificial intelligence approach volume III
Machine learning: an artificial intelligence approach volume III
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Learning Concepts in Parallel Based Upon the Strategy of Version Space
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Discovering fuzzy inter- and intra-object associations
Expert Systems with Applications: An International Journal
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Knowledge acquisition by interviewing a domain expert is one of the most problematic aspects of the development of expert systems. As an alternative, methods for inducing concept descriptions from examples have proven useful in eliminating this bottleneck. In this paper, we propose a probabilistic induction method (PIM), which is an improvement of the Chan and Wong method, for detecting relevant patterns implicit in a given data set. PIM uses the technique of residual analysis and several heuristics to effectively detect complex relevant patterns and to avoid the problem of combinatorial explosion. A reasonable trade-off between the induction time and the classification ratio is achieved. Moreover, PIM quickly classifies unknown objects using classification rules converted from the positively relevant patterns detected. Three experiments are conducted to confirm the validity of PIM.