Three-way decisions method based on matrices approaches oriented dynamic interval-valued information system

Abstract

Interval-valued Information Systems (IvIS) reflect the uncertain information in real scene, in which the attribute value of objects are all interval values rather than single values. Data information analysed by three-way decisions is not static but dynamically changing in IvIS, which results in the updating of positive region, boundary region and negative region of decision class X. In this paper, a matrix computational framework based on λ-similarity relation is proposed on the variation of the attribute set, attribute value and object set. Based on the framework, some incremental algorithms are proposed to calculate the positive, boundary and negative region of X in dynamic IvIS. Finally, comparative experiments on data sets from UCI are conducted when attribute set, attribute value and object set are updating over time, respectively. Experimental results show that in comparison with the traditional algorithm, the proposed algorithms can effectively save time for the computation of positive, boundary and negative region of X in dynamic IvIS.