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| Optimal Dimension of Dimensionality Reduction of Atmospheric Corrosion Data |
| Received:August 30, 2018 Revised:October 30, 2018 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/issn.1672-9242.2020.03.019 |
| KeyWord:atmospheric corrosion data dimensionality reduction method optimal dimension manifold learning ensemble learning |
| Author | Institution |
| PAN Ji-qing |
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing , China |
| FU Dong-mei |
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing , China |
| YANG Tao |
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing , China |
| LIU Lei-ming |
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing , China |
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| Abstract: |
| The work aims to determine the optimal dimension for the dimensionality reduction of metals' atmospheric corrosion data. The four methods such as PCA, MDS, Isomap and LLE were used for the dimensionality reduction of atmospheric corrosion data, and an ensemble learning algorithm was used to establish the prediction model. For different dimensionality reduction methods and the calculation of the number of neighbors, the mean absolute percentage error (MAPE) was used to evaluate the prediction results, and the dimension corresponding to the best prediction rate was used as the optimal dimension. Under the action of different dimensionality reduction methods and the neighbor parameters, the optimal dimension ranged from 2 to 7 dimensions. Manifold learning method was used for the dimensionality reduction of atmospheric corrosion data, and the resulting MAPE was less than that of the linear dimensionality reduction method. The optimal dimension for each dimensionality reduction method may be different. Finally, the optimal dimension of the atmospheric corrosion data processed by the four dimensionality reduction methods is obtained through the comparison of the MAPE values. |
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