Probabilistic Rough Classification in Information Systems with Fuzzy Decision Attributes

In 2004, G. Ganesan et. al introduced the concept of indexing any information system with fuzzy decision attributes using a threshold. These indices are based on the two way approach of Pawlak-s rough sets. However, the approach of Pawlak lacks in quantifying the importance of any basic granule which involve in the approximation. Later, Y. Y. Yao discussed a new Probabilistic Rough Set model which appropriately quantifies the appropriate basic granules. In this paper, we extended the work of G. Ganesan et. al. , for the Probabilistic Rough Set Model to improve the efficiency of rough indices in the information system with fuzzy decision attributes.