Evaluation method for decision rule sets

In this paper, a decision table in rough set theory is classified into three types according to its consistency. Three parameters a (whole certainty measure), beta (whole consistency measure) and gamma (whole support measure) are introduced to evaluate the performance of a decision rule set induced from a decision table. For three types of decision tables, the dependency of the parameters upon condition/decision granulation is analyzed. The parameters can be used to construct an evaluation function in favor of selecting a better one from some different rule acquiring methods for real decision problems.