Nearest neighbor search and k-nearest neighbor graph construction are two fundamental issues arise from many disciplines such as information retrieval, data-mining, machine learning and computer vision. Despite continuous efforts have been taken in the last several decades, these two issues remain challenging. They become more and more imminent given the big data emerges in various fields and h...

Classification of objects is an important area in a variety of fields and applications. In the presence of full knowledge of the underlying joint distributions, Bayes analysis yields an optimal decision procedure and produces optimal error rates. Many different methods are available to make a decision in those cases where information of the underlying joint distributions is not presented. The k...

This paper presents an interval type-2 fuzzy Knearest neighbor (NN) algorithm that is an extension of the type1 fuzzy K-NN algorithm proposed in [l]. In our proposed method, the membership values for each vector are 'extended as interval type-2 fuzzy memberships by assigning uncertainty to the type-1 memberships. By doing so, the classification result obtained by the interval type-2 fuzzy K-NN ...

single nearest neighbour fuzzy approach 2 ABSTRACT The main aim of this paper is to introduce the single nearest neighbour approach for pattern recognition and the concept of incremental learning of a fuzzy classifier where decision making is based on data available up to time t rather than what may be available at the start of the trial, i.e. at t=0. The single nearest neighbour method is expl...