Abstract
To represent the structure of data measured at different granularity levels hierarchically, the concept of multi-scale information table or decision table has been presented from the perspective of granular computing. In this study, we mainly address the issue of optimal scale selection and rule acquisition in dominance-based multi-scale intuitionistic fuzzy (IF) decision tables. By considering a type of decision systems where condition attributes are taken as dominant IF values and those of decision attributes are symbolic dominance ones, we propose two dominance-based rough sets in multi-scale IF decision tables and discuss their optimal scale selection and rule acquisition algorithms, which are used to acquire decision rules for information system security auditing risk judgment for certified information systems auditors, candidate global supplier selection, and car classification. Unlike in the existing literature, the selection of the optimal scales and reduction are simultaneously performed in the viewpoint of local and global approximations in dominance-based multi-scale IF decision tables.
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