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...
| الحاوية / القاعدة: | ENERGY REPORTS |
|---|---|
| المؤلفون الرئيسيون: | , , , |
| التنسيق: | مقال |
| اللغة: | English |
| منشور في: |
ELSEVIER
2025
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001386454100001 |