Attribute Reduction in Stochastic Information Systems Based On α-Dominance

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Rough set has been commonly taken part in literature to examine       inadequate and incomplete information systems. The efficiency of rough set with stochastic data observed for developing convenience and scalability. In this study, we use a ranking approach for attribute reduction in stochastic information systems and generalized this via presenting a dominance relation.  We obtained the rough set approach of attribute reduction in stochastic information systems by establishing the dominance degrees. Furthermore, attribute reduction methods are studied by considering discernibility matrix and this approach is applied to explanatory examples to demonstrate its validity. Also this research proposes many research fields and new application areas show a tendency to concerning rough set approach to stochastic information systems. 

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