A fuzzy extended \(k\)-nearest neighbors rule (Q1182014)

The purpose of this paper is to describe an automatic fuzzy classification algorithm working in a partially supervised environment. This algorithm is based on the fuzzy labeling of samples by means of a membership function. As long as the membership function carries more information than the classical characteristic function, the concepts of clustering and discrimination are extended to the fuzzy field. First the learning set is fuzzily clustered. The membership function is based on a weighted \(k\)-nearest neighbors rule and does not require the optimization of any criterion. Second, the test set is analyzed and a new membership function is proposed.