- Title
- Treating class imbalance in non-technical loss detection : an exploratory analysis of a real dataset
- Creator
- Ghori, Khawaja; Awais, Muhammad; Khattak, Akmal; Imran, Muhammad; Amin, Fazal; Szathmary, Laszlo
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/185719
- Identifier
- vital:16743
- Identifier
-
https://doi.org/10.1109/ACCESS.2021.3095145
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- 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 characteristic that exists in these datasets is the imbalance distribution of the classes. When the focus is on predicting the minority class of suspect activities, the classifiers' sensitivity to the class imbalance becomes more important. In this paper, we evaluate the performance of a range of classifiers with under-sampling and over-sampling techniques. The results are compared with the untreated imbalanced dataset. In addition, we compare the performance of the classifiers using penalized classification model. Lastly, the paper presents an exploratory analysis of using different sampling techniques on NTL detection in a real dataset and identify the best performing classifiers. We conclude that logistic regression is the most sensitive to the sampling techniques as the change of its recall is measured around 50% for all sampling techniques. While the random forest is the least sensitive to the sampling technique, the difference in its precision is observed between 1% - 6% for all sampling techniques. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 9, no. (2021), p. 98928-98938
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2013 IEEE
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and computing sciences; Class imbalance; Cost-sensitive learning; Non-technical loss detection; Over-sampling; Sampling techniques; Under-sampling
- Full Text
- Reviewed
- Funder
- This work was supported in part by the European Union and the European Social Fund under Project EFOP-3.6.1-16-2016-00022, and in part by the Deanship of Scientific Research at King Saud University through Research Group under Project RG-1441-490.
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