A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection
This paper presents a novel supervised machine learning-based electric theft detection approach using the feature engineered-CatBoost algorithm in conjunction with the SMOTETomek algorithm. Contrary to the previous literature, where the missing observations in data are either ignored or imputed with...
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| 主要な著者: | , , , , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Elsevier Ltd
2021
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| 主題: | |
| オンライン･アクセス: | http://eprints.utm.my/id/eprint/95358/1/SaddamHussain2021_ANovelFeatureEngineeredCatBoost.pdf http://eprints.utm.my/id/eprint/95358/ http://dx.doi.org/10.1016/j.egyr.2021.07.008 |
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| 要約: | This paper presents a novel supervised machine learning-based electric theft detection approach using the feature engineered-CatBoost algorithm in conjunction with the SMOTETomek algorithm. Contrary to the previous literature, where the missing observations in data are either ignored or imputed with average values, this work utilizes k-Nearest neighbor technique for missing data imputation; thus, an accurate and realistic estimation of the missing data is achieved. To mitigate the biasness to the majority data class, the proposed model utilizes the SMOTETomek algorithm, which neutralizes the mentioned effect by managing a proper balance between over-sampling and under-sampling techniques. |
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