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A Representation-Based Pseudo Nearest Neighbor Classifier

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Data Science (ICPCSEE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 901))

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Abstract

K-nearest neighbor rule (KNN) is a simple and powerful classification algorithm. In this article, we develop a representation-based pseudo nearest neighbor rule (RPNN), which is based on the idea of pseudo nearest neighbor rule (PNN). In the proposed RPNN, a query point is represented as a linear combination of all the training samples in each class, and we use k largest representation coefficients to determine k nearest neighbors per class instead of Euclidean distance. Then, we design the pseudo nearest neighbor of the query point per class and compute the distance between the query point and the pseudo nearest neighbor. The query point belongs to the class which has the closest representation-based pseudo nearest neighbor among all classes. Experimental results on twelve data sets have demonstrated that the proposed RPNN classifier can improve the classification accuracy in the small sample size cases, compared to the traditional KNN-based methods.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant No. 61502208), Natural Science Foundation of Jiangsu Province of China (Grant Nos. BK20150522, BK20140571, BE2017700) and Research Foundation for Talented Scholars of JiangSu University (Grant No. 14JDG037).

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Authors and Affiliations

  1. School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, 212013, China

    Yanwei Qi

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  1. Yanwei Qi
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Correspondence to Yanwei Qi .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, Henan, China

    Qinglei Zhou

  2. Zhengzhou University of Light Industry, Zhengzhou, Henan, China

    Yong Gan

  3. Northeast Forestry University, Harbin, China

    Weipeng Jing

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. Zhengzhou Institute of Technology, Zhengzhou, China

    Yan Wang

  6. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Qi, Y. (2018). A Representation-Based Pseudo Nearest Neighbor Classifier. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_13

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  • DOI: https://doi.org/10.1007/978-981-13-2203-7_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2202-0

  • Online ISBN: 978-981-13-2203-7

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