Paper
23 May 2022 Missing data reconstruction method based on Kmeans and GBDT combined model
Jianbin Deng, Fusheng Li, Bin Guo, Ji Wang, Xingxing Feng, Ye Zhao, Fan Zhang
Author Affiliations +
Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 122542N (2022) https://doi.org/10.1117/12.2640126
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China
Abstract
Complete and available data is of great significance for improving the theoretical line loss calculation in the low-voltage transformer area. However, with the upgrading of equipment, the electrical data presents the characteristics of small granularity and high complexity, resulting in insufficient line loss calculation accuracy. This paper proposes a missing data reconstruction method based on Kmeans and GBDT combined model. Since it is difficult to unify the reconstruction models of different types of data, the data set is clustered and divided by Kmeans, and GBDT is used for training respectively. During the test, the corresponding GBDT model is used for reconstruction according to the sample category. The simulation results show that the proposed method is suitable for actual data and has higher reconstruction accuracy compared with the traditional mean filling, KNN and decision tree methods. The proposed method can reconstruct the missing of multiple datasets and has good generalization.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jianbin Deng, Fusheng Li, Bin Guo, Ji Wang, Xingxing Feng, Ye Zhao, and Fan Zhang "Missing data reconstruction method based on Kmeans and GBDT combined model", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 122542N (23 May 2022); https://doi.org/10.1117/12.2640126
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KEYWORDS
Data modeling

Reconstruction algorithms

Transformers

Data communications

Statistical modeling

Computer simulations

Expectation maximization algorithms

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