Table 12.
Electrical utilities fraud
| Title | Bridging the gap between energy consumption and distribution through non-technical loss detection |
| Description | Use CatBoost for predicting non-technical loss in power distribution networks, authors report little in terms of quantitative results |
| Performance metric | Performance metric not explicit |
| Winner | Not clear, authors do not give exact numbers |
| Reference | [29] |
| Title | Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection |
| Description | Compare CatBoost with 14 other classifiers |
| Performance metric | Precision, recall, F-Measure |
| Winner | CatBoost has highest precision and F-measure, has 0.003 higher recall |
| Reference | [52] |
| Title | Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing |
| Description | Technique for using CatBoost with highly imbalanced data |
| Performance metric | True positive rate, false positive rate |
| Winner | CatBoost, has lowest false positive rate, LightGBM wins true positive rate, CatBoost has longest total train and test time, LightGBM has shortest total train and test time |
| Reference | [31] |
| Title | Impact of feature selection on non-technical loss detection |
| Description | Use incremental feature selection, compare performance of CatBoost, Decision Tree and K-Nearest Neighbors classifiers |
| Performance metric | Precision, recall, F-Measure |
| Winner | CatBoost, except for recall of models trained with 9 features, where K-NN wins |
| Reference | [30] |