Dynamically updating approximations based on multi-threshold tolerance relation in incomplete interval-valued decision information systems

Abstract

With the development of society, data noise and other factors will cause the incompleteness of information systems. Objects may increase or decrease over time in information systems. The classical information system can be extended to the incomplete interval-valued decision information system (IIDIS) that is the researching object of this paper. Incremental learning technique is a significant method for solving approximate sets under dynamic data. This article defines a multi-threshold tolerance relation based on the set pair analysis theory and establishes a rough set model in IIDIS. Then, several methods and algorithms for statically/dynamically solving approximate sets are shown. Finally, comparative experiments from six UCI data sets show both dynamic algorithms take less time than the static algorithm to calculate the approximate sets no matter how object set changes.