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An Incremental Network with Local Experts Ensemble

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9491))

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Abstract

Ensemble learning algorithms aim to train a group of classifiers to enhance the generalization ability. However, vast of those algorithms are learning in batches and the base classifiers (e.g. number, type) must be predetermined. In this paper, we propose an ensemble algorithm called INLEX (Incremental Network with Local EXperts ensemble) to learn suitable number of linear classifiers in an online incremental mode. Specifically, it incrementally learns the representational nodes of the input space. In the incremental process, INLEX finds nodes in the decision boundary area (boundary nodes) based on the theory of entropy: boundary nodes are considered to be disordered. In this paper, boundary nodes are activated as experts, each of which is a local linear classifier. Combination of these linear experts with dynamical weights will constitute a decision boundary to solve nonlinear classification tasks. Experimental results show that INLEX obtains promising performance on real-world classification benchmarks.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61375064, 61373001 and 61321491), Foundation of Jiangsu NSF (Grant No. BK20131279).

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Authors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, China

    Shaofeng Shen, Qiang Gan, Furao Shen & Jinxi Zhao

  2. Department of Electrical and Computer Engineering, University of Detroit Mercy, Detroit, USA

    Chaomin Luo

Authors
  1. Shaofeng Shen
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  2. Qiang Gan
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  3. Furao Shen
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  4. Chaomin Luo
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  5. Jinxi Zhao
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Corresponding author

Correspondence to Furao Shen .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Shen, S., Gan, Q., Shen, F., Luo, C., Zhao, J. (2015). An Incremental Network with Local Experts Ensemble. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_58

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  • DOI: https://doi.org/10.1007/978-3-319-26555-1_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26554-4

  • Online ISBN: 978-3-319-26555-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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