Attribute reduction of incomplete information system using characteristic multigranulation model

In order to extract reliable decision rules from an incomplete information system (IIS) that simultaneously contains two kinds of unknown attribute values, an attribute reduction algorithm using characteristic multigranulation model is put forward in this paper. First, characteristic sets that satisfy characteristic relation are determined according to the IIS and the attribute subset. Calculate the attribute dependency of the decision classes with respect to the attribute subset. Then, determine whether all condition attribute values are indispensable respectively in terms of the attribute dependencies. Finally, delete all the redundant condition attribute values, and generate decision rules. So as to validate the effectiveness of the proposed algorithm, two numerical experiments were carried out. The experimental analysis indicates the proposed algorithm is more efficient and accurate.