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A New Prediction System Based on Self-Growth Belief Rule Base with Interpretability Constraints

Yingmei Li, Peng Han, Wei He*, Guangling Zhang, Hongwei Wei, Boying Zhao

School of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China

* Corresponding Author: Wei He. Email: email

Computers, Materials & Continua 2023, 75(2), 3761-3780.


Prediction systems are an important aspect of intelligent decisions. In engineering practice, the complex system structure and the external environment cause many uncertain factors in the model, which influence the modeling accuracy of the model. The belief rule base (BRB) can implement nonlinear modeling and express a variety of uncertain information, including fuzziness, ignorance, randomness, etc. However, the BRB system also has two main problems: Firstly, modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy. Secondly, interpretability is not considered in the optimization process of current research, resulting in the destruction of the interpretability of BRB. To balance the accuracy and interpretability of the model, a self-growth belief rule base with interpretability constraints (SBRB-I) is proposed. The reasoning process of the SBRB-I model is based on the evidence reasoning (ER) approach. Moreover, the self-growth learning strategy ensures effective cooperation between the data-driven model and the expert system. A case study showed that the accuracy and interpretability of the model could be guaranteed. The SBRB-I model has good application prospects in prediction systems.


Cite This Article

Y. Li, P. Han, W. He, G. Zhang, H. Wei et al., "A new prediction system based on self-growth belief rule base with interpretability constraints," Computers, Materials & Continua, vol. 75, no.2, pp. 3761–3780, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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