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Research and development of neural network ensembles: a survey

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Abstract

A Neural Network Ensemble (NNE) combines the outputs of several individually trained neural networks in order to improve generalization performance. This article summarizes different approaches on the development and the latest studies on NNE. The introduction of the basic principles of NNE is followed by detailed descriptions of individual neural network generation method, conclusion generation method and fusion based on granular computing and NNE. In addition, for each of these methods we provide a short taxonomy in terms of their relevant characteristics, and analyze several of NNE applications, classic algorithms and contributions on various fields.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No.61379101), the National Key Basic Research Program of China (No.2013CB329502), the Natural Science Foundation of Jiangsu Normal University (No. 14XLA12), National Natural Science Foundation of China (No. 61472424) and Fundamental Research Funds for the Central Universities under Grant 2013RC10.

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Authors and Affiliations

  1. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, Jiangsu, China

    Hui Li

  2. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Xuesong Wang

  3. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China

    Shifei Ding

  4. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Shifei Ding

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  1. Hui Li
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  2. Xuesong Wang
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  3. Shifei Ding
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Correspondence to Hui Li.

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Li, H., Wang, X. & Ding, S. Research and development of neural network ensembles: a survey. Artif Intell Rev 49, 455–479 (2018). https://doi.org/10.1007/s10462-016-9535-1

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  • DOI: https://doi.org/10.1007/s10462-016-9535-1

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