Treating Class Imbalance in Non-Technical Loss Detection: An Exploratory Analysis of a Real Dataset

Treating Class Imbalance in Non-Technical Loss Detection: An Exploratory Analysis of a Real Dataset

Non-Technical Loss (NTL) is a significant concern for many electric supply companies due to the financial impact caused as a result of suspect consumption activities. A range of machine learning classifiers have been tested across multiple synthesized and real datasets to combat NTL. An important ch...

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Bibliographic Details
Main Authors: Khawaja Moyeezullah Ghori, Muhammad Awais, Akmal Saeed Khattak, Muhammad Imran, Fazal-E-Amin, Laszlo Szathmary
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Class imbalance
non-technical loss detection
sampling techniques
under-sampling
over-sampling
cost-sensitive learning
Online Access:https://ieeexplore.ieee.org/document/9475464/

Internet

https://ieeexplore.ieee.org/document/9475464/

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