In search for effective granularity with DTRS
Bing Zhou, Yiyu Y. Yao · 2010
Decision Theoretic Rough Set (DTRS) model provides a three-way decision approach to classification problems, which allows the classifier to make a deferment decision on the hard cases, rather than forced to make an immediate determination. The deferred cases must be reexamined by collecting further information. Although the formulation of DTRS is intuitively appealing, a fundamental question that remains is how to determine the classification of the deferment cases. In this paper, we introduce an adaptive learning method that automatically deals with the deferred cases by searching for the effective granularity. A decision tree is constructed for classification. At each level, we sequentially choose the attributes that provide the most effective granularity. A subtree is added recursively if the conditional probability lies in between of the two threshold values. A branch reaches its leaf node until all the examples are correctly classified. This learning process is illustrated by a given example.