Attribute reduction for hybrid data based on fuzzy rough iterative computation model.

A hybrid information system (HIS) means an information system (IS) with categorical attributes, real-valued attributes and missing values. It is more difficult to deal with an HIS than an IS with a type of attributes. This paper studies attribute reduction in an HIS by means of fuzzy rough iterative computation model (FRIC-model). To more accurately express the difference between objects in an HIS, a novel distance function on the object set is first constructed. Then, fuzzy symmetric relations on the object set based on this function are established and fuzzy rough approximations are defined. Next, some evaluation functions that represent the classification ability of attribute subsets, such as fuzzy positive regions, dependency functions and attribute importance functions are introduced. After then, FRIC-model is given and an attribute reduction algorithm for hybrid data based on this model is designed. At last, numerical experiments are carried out to estimate the performance of the designed algorithm. The experimental results show that the designed algorithm is more effective than some existing algorithms.