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International Conference on Swarm Intelligence

ICSI 2017: Advances in Swarm Intelligence pp 371–378Cite as

An Improved Weighted ELM with Krill Herd Algorithm for Imbalanced Learning

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10386))

Abstract

The traditional weighted extreme learning machine chose input weights and hidden biases randomly. This made the algorithm responding slowly and led to worse generalization. In order to overcome the shortcomings, an improved weighted extreme learning machine combining with krill herd algorithm is proposed to solve the class imbalance problems. Krill herd algorithm is adopted to optimize the input weights and hidden biases. The simulation results show that krill herd algorithm can find more suitable input weights and hidden biases, which has better classify accuracy than particle swarm optimization and genetic algorithm. The proposed algorithm also has stable classify accuracy for the data sets with different imbalance ratios.

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Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant 61573361, National Basic Research Program of China under Grant 2014CB046300 and the Innovation Team of CUMT under Grant 2015QN003. Also, thank you for the support from Collaborative Innovation Center of Intelligent Mining Equipment, CUMT.

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Authors and Affiliations

  1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221008, Jiangsu, China

    Yi-nan Guo, Pei Zhang, Jian Cheng, Yong Zhang & Lingkai Yang

  2. School of Information and Control, Nanjing University of Information Science and Technology, Ning-Liu Road, Pu-Kou District, Nanjing, China

    Xiaoning Shen

  3. Department of Computer Science and Technology, Jiangnan University, Wuxi, China

    Wei Fang

Authors
  1. Yi-nan Guo
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  2. Pei Zhang
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  3. Jian Cheng
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  4. Yong Zhang
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  5. Lingkai Yang
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  6. Xiaoning Shen
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  7. Wei Fang
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Corresponding author

Correspondence to Jian Cheng .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  4. Shenzhen University, Shenzhen, China

    Ben Niu

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Cite this paper

Guo, Yn. et al. (2017). An Improved Weighted ELM with Krill Herd Algorithm for Imbalanced Learning. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_39

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  • DOI: https://doi.org/10.1007/978-3-319-61833-3_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61832-6

  • Online ISBN: 978-3-319-61833-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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