Improved pseudo nearest neighbor classification

Abstract

k-Nearest neighbor (KNN) rule is a very simple and powerful classification algorithm. In this article, we propose a new KNN-based classifier, called the local mean-based pseudo nearest neighbor (LMPNN) rule. It is motivated by the local mean-based k-nearest neighbor (LMKNN) rule and the pseudo nearest neighbor (PNN) rule, with the aim of improving the classification performance. In the proposed LMPNN, the k nearest neighbors from each class are searched as the class prototypes, and then the local mean vectors of the neighbors are yielded. Subsequently, we attempt to find the local mean-based pseudo nearest neighbor per class by employing the categorical k local mean vectors, and classify the unknown query patten according to the distances between the query and the pseudo nearest neighbors. To assess the classification performance of the proposed LMPNN, it is compared with the competing classifiers, such as LMKNN and PNN, in terms of the classification error on thirty-two real UCI data sets, four artificial data sets and three image data sets. The comprehensively experimental results suggest that the proposed LMPNN classifier is a promising algorithm in pattern recognition.