Incremental updating rough approximations in interval-valued information systems

Abstract

Interval-valued Information System (IvIS) is a generalized model of single-valued information system, in which the attribute values of objects are all interval values instead of single values. The attribute set in IvIS is not static but rather dynamically changing over time with the collection of new information. The rough approximations may evolve accordingly, which should be updated continuously for data analysis based on rough set theory. In this paper, on the basis of the similarity-based rough set theory in IvIS, we first analyze the relationships between the original approximation sets and the updated ones. And then we propose the incremental methods for updating rough approximations when adding and removing attributes, respectively. Finally, a comparative example is used to validate the effectiveness of the proposed incremental methods.