VPRSM based decision tree classifier
A new approach for inducing decision trees is proposed based on the Variable Precision Rough Set Model. From the rough set theory point of view, in the process of inducing decision trees with evaluations of candidate attributes, some methods based on purity measurements, such as information entropy based methods, emphasize the effect of class distribution. The more unbalanced the class distribution is, the more favorable it is. The rough set based approaches emphasize the effect of certainty. The more certain it is, the better. The criterion for node selection in the new method is based on the measurement of the variable precision explicit regions corresponding to candidate attributes. We compared the presented approach with C4.5 on some data sets from the UCI machine learning repository, which instantiates the feasibility of the proposed method.
rough sets variable precision explicit region variable precision implicit region and decision tree