On the evaluation of attribute information for mining classification rules

We deal with the evaluation of attribute information for mining classification rules. In a decision tree, each internal node corresponds to a decision on an attribute and each outgoing branch corresponds to a possible value of this attribute. The ordering of attributes in the levels of a decision tree will affect the efficiency of the classification process, and should be determined in accordance with the relevance of these attributes to the target class. We consider in this paper two different measurements for the relevance of attributes to the target class, i.e., inference power and information gain. These two measurements, though both being related to the relevance to the group identity, can in fact lead to different branching decisions. It is noted that, depending on the stage of tree branching, these two measurements should be judiciously employed so as to maximize the effects they are designed for. The inference power and the information gain of multiple attributes are also evaluated.