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Nearest Neighbor Search Techniques Applied in the Nearest Feature Line Classifier

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Proceedings of the Fourth Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 682))

Abstract

Pointing to the computational complexity to find the minimum distance in the nearest feature line (NFL) classification algorithm, the nearest neighbor search methods with Full Search (FS), Partial Distortion Search (PDS), Absolute Error Inequality (AEI) and Equal-average Nearest Neighbor Search (ENNS) is used to evaluate the calculated performance on NFL. The experimental results demonstrate that the computational complexity on NFL using these search techniques is different and some of the nearest neighbor search methods could improve the calculated performance on finding out the minimum distance applied in the NFL classification.

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Acknowledgment

The author wishes to thank Science Foundation (2017J01732) of the Fujian Province, China

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Authors and Affiliations

  1. Department of Information Science and Engineering, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Fang Guo & Jeng-Shyang Pan

  2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Fang Guo & Jeng-Shyang Pan

Authors
  1. Fang Guo
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  2. Jeng-Shyang Pan
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Corresponding author

Correspondence to Fang Guo .

Editor information

Editors and Affiliations

  1. Department of Computer Science, VÅ B-Technical University of Ostrava , Ostrava-Poruba, Czech Republic

    Pavel Krömer

  2. Dept. Lenguajes y Ciencias de la Computación, University of Málaga, Málaga, Spain

    Enrique Alba

  3. Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  4. Department of Computer Science, VÅ B-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Václav Snášel

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Guo, F., Pan, JS. (2018). Nearest Neighbor Search Techniques Applied in the Nearest Feature Line Classifier. In: Krömer, P., Alba, E., Pan, JS., Snášel, V. (eds) Proceedings of the Fourth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2017. Advances in Intelligent Systems and Computing, vol 682. Springer, Cham. https://doi.org/10.1007/978-3-319-68527-4_38

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  • DOI: https://doi.org/10.1007/978-3-319-68527-4_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68526-7

  • Online ISBN: 978-3-319-68527-4

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