Stepwise dynamic nearest neighbor (SDNN): a new algorithm for classification

Abstract

Although the standard k-nearest neighbor (KNN) algorithm has been used widely for classification in many different fields, it suffers from various limitations that abate its classification ability, such as being influenced by the distribution of instances, ignoring distances between the test instance and its neighbors during classification, and building a single/weak learner. This paper proposes a novel algorithm, called stepwise dynamic nearest neighbor (SDNN), which can effectively handle these problems. Instead of using a fixed parameter k like KNN, it uses a dynamic neighborhood strategy according to the data distribution and implements a new voting mechanism, called stepwise voting. Experimental results were conducted on 50 benchmark datasets. The results showed that the proposed SDNN method outperformed the KNN method, KNN variants, and the state-of-the-art methods in terms of accuracy.