Approach of Rule Extracting Based on Attribute Significance and Decision Classification

Propose a novel approach of rule extracting based on attribute significance and decision classification (REBSC). Condition attributes are only discretized with their own features in local discretization theory, and the eventual rule set normally can be achieved after attribute reduction. The REBSC approach given in this paper makes the most of each condition attribute significance and classification object, generates minimum decision rules without attribute reduction. Experiment one fully demonstrates the reasonability of the REBSC algorithm. Experiment two further proves its validity and testifies the significance of breakpoint division.