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Pseudo nearest neighbor rule for pattern classification
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DOI：10.1016/j.eswa.2008.02.003.png)
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摘要
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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 oil the unknown patterns. Experimental results confirm the validity of this new classification rule even in practical situations. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
The k-nearest neighbor classification rule (k-NNR)
Pseudo nearest neighbor classification rule (PNNR)
Distance weighted k-nearest neighbor rule
The local mean-based learning
Pseudo nearest neighbor (PNN)
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期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
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