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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(3): 242-246 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.514

Evidence Theory in Incomplete Information Tables

Yu-Ru Syau and En-Bing Lin
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 tables.

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:enbing.lin@cmich.edu).


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.

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