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Particle Swarm Optimization-Based Weighted-Nadaraya-Watson Estimator

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2018)

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Abstract

This paper proposes a Particle Swarm Optimization-based Weighted-Nadaraya-Watson (PSO-WNW) estimator which uses the standard PSO algorithm to choose the optimal weights for WNW estimator. PSO-WNW estimator gives up the weight constraints of the classical WNW estimator, which makes PSO algorithm to find the more appropriate weights. The estimation performances of PSO-WNW estimator are tested based on 6 well-known testing functions. The experimental results show that PSO-WNW estimator can further reduce the training and testing Root Mean Square Errors (RMSEs) of WNW estimator for any given bandwidth parameter and demonstrate the feasibility and effectiveness of PSO-WNW estimator.

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Notes

  1. 1.

    This paper focuses on the case of scalar-input.

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Acknowledgments

This paper was supported by National Natural Science Foundations of China (61503252 and 61473194), China Postdoctoral Science Foundation (2016T90799), Scientific Research Foundation of Shenzhen University for Newly-introduced Teachers (2018060) and Shenzhen-Hong Kong Technology Cooperation Foundation (SGLH20161209101100926).

Author information

Authors and Affiliations

  1. Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, Guangdong, China

    Jie Jiang, Yulin He & Joshua Zhexue Huang

  2. National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060, Guangdong, China

    Jie Jiang, Yulin He & Joshua Zhexue Huang

Authors
  1. Jie Jiang
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  2. Yulin He
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  3. Joshua Zhexue Huang
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Corresponding author

Correspondence to Yulin He .

Editor information

Editors and Affiliations

  1. University of Melbourne, Melbourne, VIC, Australia

    Mohadeseh Ganji

  2. University of Melbourne, Melbourne, VIC, Australia

    Lida Rashidi

  3. McGill University, Montreal, QC, Canada

    Benjamin C. M. Fung

  4. Griffith University, Gold Coast, QLD, Australia

    Can Wang

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Jiang, J., He, Y., Huang, J.Z. (2018). Particle Swarm Optimization-Based Weighted-Nadaraya-Watson Estimator. In: Ganji, M., Rashidi, L., Fung, B., Wang, C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_27

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  • DOI: https://doi.org/10.1007/978-3-030-04503-6_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04502-9

  • Online ISBN: 978-3-030-04503-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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