Abstract
The neural network is being widely applied to solve various types of real-life problems. Continuous researches are needed to improve the accuracy of the neural network for practical application. In this paper, the method of applying multiple neural network models as a way to improve the accuracy of the conventional neural network is proposed. Each of multiple neural networks is trained independently with each other, and each generated model is integrated to produce a single prediction result. The proposed prediction model with multiple neural network models is evaluated as compared to the conventional neural network model in terms of accuracy. The results of the experiment showed that the accuracy of the proposed methods was higher than that of the conventional neural network model. The use of neural networks is expected to be continuously increasing. The proposed prediction model with multiple neural network models is expected to be applied in prediction problems in real-life to improve the reliability of the prediction results.
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Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Education) (No. NRF-2017R1D1A1B03034769).
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Lim, Hi. (2021). Applying Multiple Models to Improve the Accuracy of Prediction Results in Neural Networks. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_33
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DOI: https://doi.org/10.1007/978-3-030-68449-5_33
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