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A Processor Performance Prediction Method Based on Interpretable Hierarchical Belief Rule Base and Sensitivity Analysis

Chen Wei-wei1, He Wei1,2,*, Zhu Hai-long1, Zhou Guo-hui1, Mu Quan-qi1, Han Peng1

1 College of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150500, China
2 Rocket Force University of Engineering, Xi’an, 710025, China

* Corresponding Author: He Wei. Email: email

Computers, Materials & Continua 2023, 74(3), 6119-6143.


The prediction of processor performance has important reference significance for future processors. Both the accuracy and rationality of the prediction results are required. The hierarchical belief rule base (HBRB) can initially provide a solution to low prediction accuracy. However, the interpretability of the model and the traceability of the results still warrant further investigation. Therefore, a processor performance prediction method based on interpretable hierarchical belief rule base (HBRB-I) and global sensitivity analysis (GSA) is proposed. The method can yield more reliable prediction results. Evidence reasoning (ER) is firstly used to evaluate the historical data of the processor, followed by a performance prediction model with interpretability constraints that is constructed based on HBRB-I. Then, the whale optimization algorithm (WOA) is used to optimize the parameters. Furthermore, to test the interpretability of the performance prediction process, GSA is used to analyze the relationship between the input and the predicted output indicators. Finally, based on the UCI database processor dataset, the effectiveness and superiority of the method are verified. According to our experiments, our prediction method generates more reliable and accurate estimations than traditional models.


Cite This Article

C. Wei-wei, H. Wei, Z. Hai-long, Z. Guo-hui, M. Quan-qi et al., "A processor performance prediction method based on interpretable hierarchical belief rule base and sensitivity analysis," Computers, Materials & Continua, vol. 74, no.3, pp. 6119–6143, 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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