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Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution

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Abstract

Extreme learning machine for single-hidden-layer feedforward neural networks has been extensively applied in imbalanced data learning due to its fast learning capability. Ensemble approach can effectively improve the classification performance by combining several weak learners according to a certain rule. In this paper, a novel ensemble approach on weighted extreme learning machine for imbalanced data classification problem is proposed. The weight of each base learner in the ensemble is optimized by differential evolution algorithm. Experimental results on 12 datasets show that the proposed method could achieve more classification performance compared with the simple vote-based ensemble method and non-ensemble method.

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Acknowledgments

This work is partly supported by National Natural Science Foundation of China (No. 61373127) and the State Key Laboratory for Novel Software Technology (Nanjing University) of China (No. KFKT2015B16).

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, No. 1, Liushu South Street, Ganjingzi District, Dalian, 116081, Liaoning Province, China

    Yong Zhang, Bo Liu, Jing Cai & Suhua Zhang

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Yong Zhang

Authors
  1. Yong Zhang
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  2. Bo Liu
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  3. Jing Cai
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  4. Suhua Zhang
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Correspondence to Yong Zhang.

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Zhang, Y., Liu, B., Cai, J. et al. Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution. Neural Comput & Applic 28 (Suppl 1), 259–267 (2017). https://doi.org/10.1007/s00521-016-2342-4

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  • DOI: https://doi.org/10.1007/s00521-016-2342-4

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