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A Quantitative Noise Method to Evaluate Machine Learning Algorithm on Multi-Fidelity Data
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Xiaotong LIU1, 2, Ziming WANG2, Jiahua OUYANG3, Tao YANG1, 2
Journal of the Chinese Ceramic Society | 2023, 51(2) : 405 - 410
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Journal of the Chinese Ceramic Society | 2023, 51(2): 405-410
• Research Articles •
A Quantitative Noise Method to Evaluate Machine Learning Algorithm on Multi-Fidelity Data
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Xiaotong LIU1, 2, Ziming WANG2, Jiahua OUYANG3, Tao YANG1, 2
Affiliations
  • 1.Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Information Science and Technology University, Beijing 100101, China
  • 2.School of Computer, Beijing Information Science and Technology University, Beijing 100101, China
  • 3.School of Information Science and Technology, Jinan University, Guangzhou 511442, China
  • LIU Xiaotong (1987-), male, Ph.D. E-mail:

About Author:

LIU Xiaotong (1987-), male, Ph.D. E-mail:

Published: 2023-02-20 doi: 10.14062/j.issn.0454-5648.20220811
Outline
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Most data in material science are multi-fidelity data. From the viewpoint of data producer, there is a system error for any quantum method. For machine learning algorithm, as a data consumer, various methods have been designed to maximize the number of knowledges extracted from the multi-fidelity data. In this paper, a quantitative method of noise addition was used to evaluate the influence of different noise types and intensities on some multi-fidelity data learning methods. And the effective scope of the data correction method was verified via iterative noise reduction. The results show that the ways to exploit the multi-fidelity data are crucial. It is necessary to consider comprehensively both the size and the noise level of the datasets. On a variety of datasets constructed with different noise types and intensities, the "Onion" training method that gradually deletes lower fidelity data is better than the "one by one" training method in the direction of noise reduction due to the synergistic effect of different multi-fidelity data. No matter what kind of noise intensity and training method, linear noise has less impact on the final performance of model. However, the data with sampled noise added, which the final testing results are similar to the real multi-fidelity data, were recommended to be adopted in a future research. Also, the complex noise in data is difficult to be corrected by a small amount of true data, thus being more suitable for the iterative noise reduction processing.

multi-fidelity  /  property prediction  /  machine learning  /  quantitative noise
刘晓彤, 王滋明, 欧阳嘉华, 杨涛. 多保真度数据学习算法的定量噪声评价. 硅酸盐学报, 2023 , 51 (2) : 405 -410 . DOI: 10.14062/j.issn.0454-5648.20220811
Xiaotong LIU, Ziming WANG, Jiahua OUYANG, Tao YANG. A Quantitative Noise Method to Evaluate Machine Learning Algorithm on Multi-Fidelity Data[J]. Journal of the Chinese Ceramic Society, 2023 , 51 (2) : 405 -410 . DOI: 10.14062/j.issn.0454-5648.20220811
Year 2023 volume 51 Issue 2
30
4
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Article Info
doi: 10.14062/j.issn.0454-5648.20220811
  • Receive Date:2022-09-29
  • Online Date:2026-10-20
  • Published:2023-02-20
Article Data
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History
  • Received:2022-09-29
  • Revised:2022-11-05
Funding
Affiliations
    1.Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Information Science and Technology University, Beijing 100101, China
    2.School of Computer, Beijing Information Science and Technology University, Beijing 100101, China
    3.School of Information Science and Technology, Jinan University, Guangzhou 511442, China

Corresponding:

YANG Tao (1980-), female, Ph.D. E-mail:
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
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Percentage of
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Genus
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Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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