A new two-layer nearest neighbor selection method for kNN classifier

Abstract The k -nearest neighbor (kNN) classifier is a classical classification algorithm that has been applied in many fields. However, the performance of the kNN classifier is limited by a simple neighbor selection method, called nearest neighbor (NN) rule, where only the neighborhood of the query is considered when selecting the nearest neighbors of the query. In other words, the NN rule only uses one-layer neighborhood information of the query. In this paper, we propose a new neighbor selection method based on two-layer neighborhood information, called two-layer nearest neighbor (TLNN) rule. The neighborhood of the query and the neighborhoods of all selected training instances in this neighborhood are considered simultaneously, then the two-layer nearest neighbors of the query are determined according to the distance, distribution relationship, and backward nearest neighbor relationship between the query and all selected training instances in the above neighborhoods. In order to verify the effectiveness of the proposed TLNN rule, a k -two-layer nearest neighbor (kTLNN) classifier is proposed to measure the classification ability of the two-layer nearest neighbors. Extensive experiments on twenty real-world datasets from UCI and KEEL repositories show that the kTLNN classifier outperforms not only the kNN classifier but also seven other state-of-the-art NN-based classifiers.