ABSTRACT
Rough Set Theory (RST) is a data analysis theory and a new mathematical tool for dealing with vague, inconsistent, incomplete information. The classical model (Pawlak-Model) (Pawlak 1982) is a qualitative one so has some limitations, such as no fault-tolerant capabilities. Thus, the construction and development of quantitative models hold significance. The Variable Precision Rough Set (VPRS) (Ziarko 1993) and graded rough set (GRS) (Yao & Lin 1996) serve as two fundamental quantitative models. VPRS and GRS have the relative and absolute fault-tolerant capabilities, respectively, and they exhibit both in-depth studies and wide applications. VPRS-Reduction were studied (Inuiguchi et al. 2009, Wang & Zhou 2009, Mi et al. 2004) and VPRS was applied to the practical geoscience and psychology fields (Yanto et al. 2012, Xie et al. 2011), while the GRS model construction was also discussed (Liu et al. 2012). VPRS and GRS exhibit a close relationship due to basic quantitative expansion; thus, a systematic comparative study of both models was made to provide their relationship, transformation, and similar properties (Zhang et al. 2012).
