The problem of classification in information systems has been studied by many authors, and different methods have been developed. The combination of rough sets and fuzzy logic for classification is a widely adopted method, as well as the use of entropy and neural networks. When information is diffuse and the number of collected data for each attribute is large, so will be the number of rules for defining different classes. Even worst is hidden information in the data set that makes the process complicated. Due to these facts, an interval of values is defined for each attribute, which is the rage between the minimum and the maximum values in the database or the interval defined by the standard deviation. This is what is defined as interval-valued information systems. Different from other works, the concept of information measure is used in this paper, together with a fuzzy logic discrimination tool. Based on these concepts, initially an attribute reduction is carried out and then fuzzy logic is applied for discriminating among the possible solutions. This proposed method is simpler than others, and gives accuracy not less than the usually employed methods.