LEARNING DECISION RULES FROM UNCERTAIN DATA USING ROUGH SETS

In this paper, we deal with the problem of learning decision rules from partially uncertain data based on rough sets. The uncertainty exists in the decision attribute and not in condition attribute values of the decision system. This latter is represented by the belief function theory. So, we will adapt the basic concepts of rough sets in order to generate rules, denoted belief decision rules.