A new nearest neighbor classifier via fusing neighborhood information

The nearest neighbor (NN) classification is a classical and yet effective technique in machine learning and data mining communities. However, its performance depends crucially on the distance function used to compute distance between samples. In this paper, we first define the concept of sample׳s neighborhood and present two related criteria according to neighborhood influence. Then, the influence of sample׳s neighborhood is comprehensively considered when computing the distances between the query and training samples. Finally, we propose an improved nearest neighbor classification algorithm via fusing neighborhood information. The proposed method can precisely characterize the distance among samples as well as enhance the predictive power of classifier to some extent. The experimental results show that the proposed algorithm basically outperforms classical nearest neighbor classifier and some other state-of-the-art classification methods.