A new locally adaptive K-nearest centroid neighbor classification based on the average distance

Abstract

The classification performance of a k-nearest neighbour (KNN) method is dependent on the choice of the k neighbours of a query. However, it is difficult to optimise the performance of KNN by choosing appropriate neighbours and an appropriate value of k. Moreover, the performance of KNN suffers from the use of a simple majority voting method. To address these three issues, we propose a new locally adaptive k-nearest centroid neighbour classification based on the average distance (AD-LAKNCN) in this paper. First, the k neighbours of the query based on the nearest centroid neighbour (NCN) are found, and the discrimination classes with different k values are derived from the number and distribution of each class of neighbours considered in the query. Then, based on the distribution information in the discrimination class for each k, the adaptive k and the final classification result are obtained. The experimental results based on 24 real-world datasets show that the new method achieves better classification performance than nine other state-of-the-art KNN algorithms.