Analysis of Classification in Interval-Valued Information Systems

The problem of classification has been studied by many authors, and different methods have been developed. Frequently the data for the different attributes is obtained from sources, where the obtained values for each attribute may change due to noise, equipment imprecision, etc. Under this condition becomes useful to define intervals, where those parameters may change values. Using these intervals, and applying rough sets, it is possible in many cases to optimize or reduce the number of attributes that will be used in the classification. When the object can not be completely determined from the rules obtained from the rough set analysis, fuzzy logic results a useful approach. This paper analyzes the conditions for which the classification is possible using a combination of rough sets and fuzzy logic. Examples are included for clarification purposes.