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Correlated Online k-Nearest Neighbors Regressor Chain for Online Multi-output Regression

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Neural Information Processing (ICONIP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14449))

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Abstract

Online multi-output regression is a crucial task in machine learning with applications in various domains such as environmental monitoring, energy efficiency prediction, and water quality prediction. This paper introduces CONNRC, a novel algorithm designed to address online multi-output regression challenges and provide accurate real-time predictions. CONNRC builds upon the k-nearest neighbor algorithm in an online manner and incorporates a relevant chain structure to effectively capture and utilize correlations among structured multi-outputs. The main contribution of this work lies in the potential of CONNRC to enhance the accuracy and efficiency of real-time predictions across diverse application domains. Through a comprehensive experimental evaluation on six real-world datasets, CONNRC is compared against five existing online regression algorithms. The consistent results highlight that CONNRC consistently outperforms the other algorithms in terms of average Mean Absolute Error, demonstrating its superior accuracy in multi-output regression tasks. However, the time performance of CONNRC requires further improvement, indicating an area for future research and optimization.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Zipeng Wu & Chu Kiong Loo

  2. School of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520, Thailand

    Kitsuchart Pasupa

Authors
  1. Zipeng Wu
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  2. Chu Kiong Loo
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  3. Kitsuchart Pasupa
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Corresponding author

Correspondence to Kitsuchart Pasupa .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Biao Luo

  2. Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Wu, Z., Loo, C.K., Pasupa, K. (2024). Correlated Online k-Nearest Neighbors Regressor Chain for Online Multi-output Regression. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_3

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  • DOI: https://doi.org/10.1007/978-981-99-8067-3_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8066-6

  • Online ISBN: 978-981-99-8067-3

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