Research on machine learning strategy based on voting model

Shipei Du; Wenhui Ding; Dongjie Yang; Lei Yang

doi:10.1117/12.2662004

28 December 2022 Research on machine learning strategy based on voting model

Shipei Du, Wenhui Ding, Dongjie Yang, Lei Yang

Author Affiliations +

Proceedings Volume 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022); 125062X (2022) https://doi.org/10.1117/12.2662004
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2022), 2022, Beijing, China

Abstract

In this research, machine learning algorithms such as decision tree, random forest, and BP neural network are used to predict a certain dataset, and then a voting prediction model is built based on the above three machine learning algorithms. To verify the performance of this voting model, we introduced confusion matrix and F1 score to evaluate the effectiveness of machine learning. The experimental results show that the performance of the machine learning strategy based on the voting model outperforms that of a single machine learning algorithm and that adjusting the voting weights of a single algorithm can also affect the performance of the whole model. This result is well worth further study.

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Shipei Du, Wenhui Ding, Dongjie Yang, and Lei Yang "Research on machine learning strategy based on voting model", Proc. SPIE 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), 125062X (28 December 2022); https://doi.org/10.1117/12.2662004

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KEYWORDS

Machine learning

Neural networks

Performance modeling

Data modeling

Evolutionary algorithms

Scanning transmission electron microscopy

Data conversion

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