Decision Making in Fuzzy Rough Set Theory

Abstract

Decision rules are powerful tools to manage information and to provide descriptions of data sets; as a consequence, they can acquire a useful role in decision-making processes where fuzzy rough set theory is applied. This paper focuses on the study of different methods to classify new objects, which are not considered in the starting data set, in order to determine the best possible decision for them. The classification methods are supported by the relevance indicators associated with decision rules, such as support, certainty, and credibility. Specifically, the first one is based on how the new object matches decision rules that describe the data set, while the second one also takes into account the representativeness of these rules. Finally, the third and fourth methods take into account the credibility of the rules compared with the new object. Moreover, we have shown that these methods are richer alternatives or generalize other approaches given in the literature.