Pseudo nearest neighbor rule for pattern classification

Authors:
Yong Zeng;Yupu Yang;Liang Zhao
Affiliations:
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 8
Cited 1

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Compression and Local Metrics for Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
A class-dependent weighted dissimilarity measure for nearest neighbor classification problems

Pattern Recognition Letters
A local mean-based nonparametric classifier

Pattern Recognition Letters
Learning prototypes and distances: A prototype reduction technique based on nearest neighbor error minimization

Pattern Recognition
Considerations about sample-size sensitivity of a family of editednearest-neighbor rules

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nearest neighbor pattern classification

IEEE Transactions on Information Theory

Clustered-Hybrid Multilayer Perceptron network for pattern recognition application

Applied Soft Computing

Quantified Score

Hi-index	12.05

Visualization

Abstract

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.