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Table 12 Electrical utilities fraud

From: CatBoost for big data: an interdisciplinary review

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, \(\text {ANN}\) 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]