Uncertain database retrieval with measure – Based belief function attribute values under intuitionistic fuzzy set

Abstract

The uncertain database retrieval with measure-based belief function attribute values can resolve concerns of database retrieval and decision making, particularly in the circumstances of classical fuzzy set, which has an assuring perspective. However, how to implement the design to explore the things based on the intuitionistic fuzzy set (IFS) in an ambiguous database is yet an open problem. This paper addresses the issues of database retrieval based on the uncertain database with the things related to IFS. In the design, a query associated with each attribute of the objects is located in terms of IFS. Based on the gathered data, the “plausibilities (Pl)” and “beliefs (Bel)” measures are measured for each attribute and then aggregated with the cooperation of the Choquet integral (CI) operator. The “satisfaction degree” of the query in the form of an interval is circumscribed as [Bel, Pl]. The defuzzified value of this interval is affirmed with the aid of golden rule representative value, to link these satisfaction degrees. Hence, the appearance of the stated algorithm is more trustworthy than the others under an unpredictable environment. The description of the asserted algorithm is illustrated with an application correlated to the library database.