Algorithms on Parameterized Rough-Intuitionistic Fuzzy Classification using a Threshold

Abstract

Hybridization of Rough Sets with intuitionistic fuzzy sets is commonly in use for deriving several real time applications. However, due to limitations, the parameterized rough sets are found to be more effective than conventional RS Models due to its quality and accuracy. In this aspect, we describe three types of algorithms namely lower, upper and parameterized rough indexing algorithms to index the records of decision table with intuitionistic fuzzy decision attributes and the same are implemented using C Programming.