An Attribute-Value Block Based Method of Acquiring Minimum Rule Sets; A Granulation Method to Construct Classifier

摘要

Decision rule acquisition is one of the important topics in rough set theory and is drawing more and more attention. In this paper, decision logic language and attribute-value block technique are introduced first. And then realization methods of rule reduction and rule set minimum are relatively systematically studied by using attribute-value block technique, and as a result effective algorithms of reducing decision rules and minimizing rule sets are proposed, which, together with related attribute reduction algorithm, constitute an effective granulation method to acquire minimum rule sets, which is a kind classifier and can be used for class prediction. At last, related experiments are conducted to demonstrate that the proposed methods are effective and feasible.