Incomplete Dominance-based Intuitionistic Fuzzy Rough Sets and their Application in Estimation of Inflation Rates in the Least Developed Countries

Although the concepts of imprecision are captured by both rough sets and intuitionistic fuzzy sets, investigations combining these two ideas their applications in incompletely ordered information systems scarce. The semantics many kinds missing still lacks consensus on a global level. Rule extraction is also an important task sort decision system which condition attributes treated as values crisp ones. main goal this paper to address semantic issues related incomplete information. This contributes following aspects: First, four types classified (i.e., “do-not-care value”, “partially-known value” , “class-specific “non-applicable value”), then complete introduced using novel semantics, followed ranking approach create each object’s neighborhood for attributes. Further, dominance-based table proposed. Second, lower upper approximation object classes validated determined comparing relationships. Third, rule developed discernibility matrix function collect knowledge from existing dominance tables. Finally, provided used estimation inflation rates LDCs with inadequate data.