Improving transformer failure classification on imbalanced DGA data using data-level techniques and machine learning
This study addresses the challenge of imbalanced dissolved gas analysis (DGA) data in transformer failure classification by assessing the impact of data-level balancing techniques on machine learning performance. Five data-level strategies - Random Under-Sampling (RUS), Edited Nearest Neighbors (ENN...
| 出版年: | ENERGY REPORTS |
|---|---|
| 主要な著者: | , , , |
| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
ELSEVIER
2025
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| 主題: | |
| オンライン･アクセス: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001386454100001 |