Weighted passive nearest neighbor algorithm: A newly-developed supervised classifier

K nearest neighbor algorithm (k-NN) is an instance-based lazy classifier that does not need to delineate the entire boundaries between classes. Thus some classification tasks that constantly need a training procedure may favor k-NN if high efficiency is needed. However, k-NN is prone to be affected by the variation of datum densities among different classes. In this paper, we define a new neighborhood relationship, called passive nearest neighbor relationship, which is demonstrated to be able to counteract with the variation of datum densities. Based on which we develop a new classifier called weighted passive nearest neighbor algorithm (WPNNA). The classifier is evaluated by 10-fold cross-validation on 10 randomly chosen benchmark datasets. The experimental results show that WPNNA performs better than other classifiers on some benchmark datasets, indicating that WPNNA is at least a good complement to the current state-of-the-art of classification.