A pre-averaged pseudo nearest neighbor classifier

Abstract

Abstract K nearest neighbor rule is a powerful classification method. However, its classification performance will be affected in the situation of small-size samples with existing outliers. To address this issue, a pre-averaged pseudo nearest neighbor classifier (PAPNN) is proposed to improve classification performance. In the PAPNN rule, pre-averaged categorical vectors are calculated by taking the average of any two points of the training sets in each class, and then k pseudo nearest neighbors are chosen from the preprocessed vectors of every class to determine the category of a query point. The pre-averaged vectors can reduce the negative impact of outliers in some degree. Extensive experiments are carried out on nineteen numerical real data sets and three artificial data sets by comparing PAPNN to other five KNN-based methods. The experimental results demonstrate that the proposed PAPNN rule is effective for classification task in the case of small-size samples with existing outliers.