Abstract—In this paper, we study rough set approximations
in an incomplete information table via a generalized model of
Ziarko’s variable precision rough set model, called Variable
Precision Generalized Rough Set (VPGRS) model. Viewing
the lower and upper approximations in VPGRS model
as mappings from(the power set of the universe of
discourse) to itself, we show that they are mutually dual, and
that both of them are order-preserving. We then introduce the
belief and plausibility functions, respectively, over U, based on
the lower and upper approximations, respectively, in
VPGRS model, and we incorporate the concepts of evidence
theory and VPGRS model to examine incomplete information
Index Terms—Rough sets, belief functions, reflexive relations, variable precision rough set models, lower and upper approximations.
Y. R. Syau is with the Department of Information Management, National Formosa University, Huwei 63201, Yunlin, Taiwan.
E. B. Lin is with Department of Mathematics, Central Michigan University, Mt. Pleasant, Michigan 48859, USA (e-mail:firstname.lastname@example.org).
Cite: Yu-Ru Syau and En-Bing Lin, "Evidence Theory in Incomplete Information Tables," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 242-246, 2015.