Structure learning for belief rule base expert system: A comparative study
Knowledge-Based Systems
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
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Use of rough sets theory to select essential attributes that can represent the original data set is well known. A reduct is the subset of the original data set that contains the essential attributes. Decision rules generated from reducts can fully describe a data set. We introduce a new method of evaluating important rules by taking advantage of rough sets theory. We consider rules generated from the original data set as attributes in the new constructed decision table. Reducts generated from this new decision table contain essential attributes, which are the rules. Only important rules are contained in the reducts. Experiments on an artificial data set, UCI data sets, and real-world data sets show that the reduct rules are more important, and this new method provides an automatic and effective way of ranking rules. © 2009 Wiley Periodicals, Inc.