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Selective Ensemble of RBFNNs Based on Improved Negative Correlation Learning

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Machine Learning and Cybernetics (ICMLC 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 481))

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Abstract

In this paper, a novel selective ensemble method based on the improved negative correlation learning is proposed. To make the proposed ensemble strategy more robust against noise, correntropy is utilized to substitute mean square error (MSE). Moreover, an L1-norm based regularization term of ensemble weights is incorporated into the objective function of the proposed ensemble strategy to fulfill the task of selective ensemble. The half-quadratic optimization technique and the surrogate function method are used to solve the optimization problem of the proposed ensemble strategy. Experimental results on two synthetic data sets and the five benchmark data sets demonstrate that the proposed method is superior to the single radial basis function neural network (RBFNN).

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Author information

Authors and Affiliations

  1. Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Baoding, 071002, Hebei Province, China

    Hongjie Xing, Lifei Liu & Sen Li

Authors
  1. Hongjie Xing
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  2. Lifei Liu
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  3. Sen Li
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Corresponding author

Correspondence to Hongjie Xing .

Editor information

Editors and Affiliations

  1. Hebei University, Baoding, China

    Xizhao Wang

  2. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  3. South China University of Technology, Guangzhou, China

    Patrick Chan

  4. Hebei University, Baoding, China

    Qiang He

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Xing, H., Liu, L., Li, S. (2014). Selective Ensemble of RBFNNs Based on Improved Negative Correlation Learning. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_31

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  • DOI: https://doi.org/10.1007/978-3-662-45652-1_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45651-4

  • Online ISBN: 978-3-662-45652-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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