Dynamic Reduct from Partially Uncertain Data Using Rough Sets

In this paper, we deal with the problem of attribute selection from a sample of partially uncertain data. The uncertainty exists in decision attributes and is represented by the Transferable Belief Model (TBM), one interpretation of the belief function theory. To solve this problem, we propose dynamic reduct for attribute selection to extract more relevant and stable features for classification. The reduction of the uncertain decision table using this approach yields simplified and more significant belief decision rules for unseen objects.