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International Conference on Neural Information Processing

ICONIP 2020: Neural Information Processing pp 30–37Cite as

Adaptive Ensemble Variants of Random Vector Functional Link Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1333))

Abstract

In this paper, we propose a novel adaptive ensemble variant of random vector functional link (RVFL) networks. Adaptive ensemble RVFL networks assign different weights to the sub-classifiers according to prediction performance of single RVFL network. Generic Adaptive Ensemble RVFL is composed of a series of unrelated, independent weak classifiers. We also employ our adaptive ensemble method to the deep random vector functional link (dRVFL). Each layer in dRVFL can be regarded as a sub-classifier. However, instead of training several models independently, the sub-classifiers of dRVFL can be obtained by training a single network once.

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Author information

Authors and Affiliations

  1. Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Minghui Hu, Qiushi Shi & P. N. Suganthan

  2. Indian Institute of Technology Indore, Simrol, Indore, 453552, India

    M. Tanveer

Authors
  1. Minghui Hu
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  2. Qiushi Shi
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  3. P. N. Suganthan
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  4. M. Tanveer
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Corresponding author

Correspondence to P. N. Suganthan .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

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

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

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Cite this paper

Hu, M., Shi, Q., Suganthan, P.N., Tanveer, M. (2020). Adaptive Ensemble Variants of Random Vector Functional Link Networks. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_4

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  • DOI: https://doi.org/10.1007/978-3-030-63823-8_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63822-1

  • Online ISBN: 978-3-030-63823-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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