Pseudo nearest neighbor rule for pattern classification

In this paper, we propose a new pseudo nearest neighbor classification rule (PNNR). It is different from the previous nearest neighbor rule (NNR), this new rule utilizes the distance weighted local learning in each class to get a new nearest neighbor of the unlabeled pattern-pseudo nearest neighbor (PNN), and then assigns the label associated with the PNN for the unlabeled pattern using the NNR. The proposed PNNR is compared with the k-NNR, distance weighted k-NNR, and the local mean-based nonparametric classification [Mitani, Y., & Hamamoto, Y. (2006). A local mean-based nonparametric classifier. Pattern Recognition Letters, 27, 1151-1159] in terms of the classification accuracy on the unknown patterns. Experimental results confirm the validity of this new classification rule even in practical situations.

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