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DF classification algorithm for constructing a small sample size of data-oriented DF regression model

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Abstract

The deep forest (DF) model is built using a multilayer ensemble of forest units through decision tree aggregation. DF presents characteristics of an easy-to-understand structure, is suitable for small sample data, and has become an important research direction in the field of deep learning. These attributes are particularly suitable for the modeling of difficult-to-measure parameters in actual industrial process. However, existing methods have mainly focused on the problem of DF classification (DFC) and cannot be directly applied to regression modeling. To overcome these issues, a survey on the DFC algorithm is presented in terms of constructing a small sample data-oriented DF regression (DFR) model for industrial processes. Hence, a survey on the DFC algorithm is presented to construct a small sample size of the data-oriented DF regression (DFR) model for industrial processes. First, principle and properties of DFC are introduced in detail to demonstrate the non-neural network deep learning model. Second, methods of DFC are discussed in terms of feature engineering, representation learning, learner selection, weighting strategy, and hierarchical structure. Furthermore, related studies on decision tree algorithm are reviewed and future investigations on DFR and its relationship with deep learning are discussed and analyzed in detail. Finally, conclusions and the future direction of this study for industrial process modeling are presented. Developing a DFR algorithm with characteristics of dynamic adaptive and interpretation abilities and lightweight structure on the basis of actual industrial domain knowledge will be the focus of our follow-up investigation. Moreover, the existing research results of DFC and deep learning can provide guidance for the future investigations on the DFR model.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (62073006 and 62021003), Beijing Natural Science Foundation (4212032), and Beijing Key Laboratory of Process Automation in Mining & Metallurgy (BGRIMM-KZSKL-2020-02).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Heng Xia, Jian Tang & Junfei Qiao

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

    Heng Xia, Jian Tang & Junfei Qiao

  3. School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Jian Zhang

  4. Departamento de Control Automatico, CINVESTAV-IPN (National Polytechnic Institute), 07360, Mexico City, Mexico

    Wen Yu

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  1. Heng Xia
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Correspondence to Jian Tang.

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Appendix

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Table 2 Abbreviations and meanings
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Table 3 Pseudocode of the RF algorithm
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Table 4 Pseudocode of the GBDT algorithm
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Xia, H., Tang, J., Qiao, J. et al. DF classification algorithm for constructing a small sample size of data-oriented DF regression model. Neural Comput & Applic 34, 2785–2810 (2022). https://doi.org/10.1007/s00521-021-06809-7

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