Combining k-nearest neighbor and centroid neighbor classifier for fast and robust classification

Abstract

The k-NN classifier is one of the most known and widely used nonparametric classifiers. The k-NN rule is optimal in the asymptotic case which means that its classification error aims for Bayes error if the number of the training samples approaches infinity. A lot of alternative extensions of the traditional k-NN have been developed to improve the classification accuracy. However, it is also well-known fact that when the number of the samples grows it can become very inefficient because we have to compute all the distances from the testing sample to every sample from the training data set. In this paper, a simple method which addresses this issue is proposed. Combining k-NN classifier with the centroid neighbor classifier improves the speed of the algorithm without changing the results of the original k-NN. In fact usage confusion matrices and excluding outliers makes the resulting algorithm much faster and robust.