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Broad learning system based ensemble deep model

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Abstract

Broad learning system (BLS) demonstrates a novel structure of neural networks based on random vector functional link network (RVFL), which has a faster modeling speed, better generalization ability, higher regression accuracy for solving regression tasks. To improve the feature representation capacity of BLS and guarantee the training efficiency, a novel ensemble deep model based on broad learning system is proposed, named as EDBLS. Unlike other deep or ensemble models of BLS, the results of EDBLS can be calculated by training the model only once. Meanwhile, the performance of BLS mainly depends on its structure, it is the same as EDBLS, we need to select network parameters for different real-world problems. As the lower efficiency of grid search, it is no longer suitable for EDBLS with more parameters. Therefore, coot optimization algorithm (COOT) is applied to determine the network structure of EDBLS for regression problems, it is important to note that the COOT is only a tool for determining the structure of EDBLS. Finally, the experimental results on MNIST, NORB and some regression benchmark datasets show that EDBLS has a better classification and regression performance, COOT is beneficial to select the network parameters of EDBLS for regression.

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Acknowledgements

This work is supported by the National Natural Science Foundations of China (No. 61976216 and No. 61672522).

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Authors and Affiliations

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

    Chenglong Zhang, Shifei Ding, Lili Guo & Jian Zhang

  2. Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Shifei Ding

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  1. Chenglong Zhang
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  2. Shifei Ding
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  3. Lili Guo
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  4. Jian Zhang
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Correspondence to Shifei Ding or Lili Guo.

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Zhang, C., Ding, S., Guo, L. et al. Broad learning system based ensemble deep model. Soft Comput 26, 7029–7041 (2022). https://doi.org/10.1007/s00500-022-07004-z

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