Active and 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 underlying data distribution. In this paper, we define a new neighborhood relationship, called passive nearest neighbors, which is deemed to be able to counteract with the variation of data densities. Based on which we develop a new classifier called active and passive nearest neighbor algorithm (APNNA). The classifier is evaluated by 10-fold cross-validation on 10 randomly chosen benchmark datasets. The experimental results show that APNNA performs better than other classifiers on some datasets and worse on some other datasets, indicating that APNNA is a good complement to the current state-of-the-art of classification.