Affiliations: [a] Key Laboratory of High Performance Computing and Stochastic Information Processing (HPCSIP) (Ministry of Education of China) and College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, P.R. China | [b] School of Computer Science and Technology, Tianjin University, Tianjin, P.R. China | [c] College of Science, Guangxi University for Nationalities, Nanning, Guangxi, P.R. China | [d] Center for the Study of Language and Cognition, Zhejiang University, Hangzhou, P.R. China
Abstract: Interval-valued information systems are general models of single-valued information systems. Interval-values appear to a way to describe the uncertainty that affects the observed objects. However, there are relatively few studies on incomplete interval-valued data. The aim of this paper is to present a dominance-based fuzzy rough set approach to incomplete interval-valued information systems. A fuzzy dominance relation which aims to describe the degree of dominance in terms of pairs of objects is proposed. Based on the proposed relation, we extend the definitions of fuzzy approximation operators and investigate the uncertainty measurement issue. A new form of fuzzy conditional entropy to measure attribute importance is presented. Meanwhile, a corresponding heuristic attribute reduction algorithm is constructed for incomplete interval-valued decision systems. Experiments show that the presented fuzzy conditional entropy and the proposed algorithm are effective.
