A new probabilistic induction method

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Title:	A new probabilistic induction method
Authors:	R. H. Hou;T. P. Hong;S. S. Tseng;S. Y. Kuo
Contributors:	Department of Information Science and Applications
Date:	1997
Issue Date:	2009-11-30 08:03:10 (UTC+0)
Publisher:	Asia University
Abstract:	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.
Relation:	Journal of Automated Reasoning 18(1):5-24
Appears in Collections:	[Department of Applied Informatics and Multimedia] Journal Article

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