The problem of classification has been studied by many authors, and different methods have been developed. In this paper a combination of rough sets and fuzzy logic for classification is adopted. Rough set theory helps in minimizing the number of attributes that influence the selection. Using this technique, a group of rules can be extracted. When information is diffuse and the number of obtained values for each attribute is large, so is the number of rules. Even worst is hidden information in the data that makes the process complicated. Due to this fact, an interval of values is defined for each attribute, moving from the minimum to the maximum obtained values in the database. This is what is defined as interval-valued information systems. For discriminating between solutions that may give more than one possible object due to their similarity, a fuzzy logic discrimination is proposed, which is simple, and gives accuracy not less than other methods.
