Fuzzy-rough nearest neighbors algorithm

In this paper the classification efficiency of the conventional K-nearest neighbors algorithm is enhanced by exploiting the fuzzy-rough uncertainty. The simplicity and nonparametric characteristics of the conventional K-nearest neighbors algorithm remain intact in the proposed algorithm. Unlike the conventional one, the proposed algorithm does not need to know the optimal value of K. Moreover, the generated class confidence values, which are interpreted in terms of the fuzzy-rough ownership values, do not necessarily summed up to one. Consequently, the proposed algorithm can distinguish between equal evidence and ignorance, and thus makes the semantics of the class confidence values richer