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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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Main Authors: Hussain, Saddam, Mustafa, Mohd. Wazir, A. Jumani, Touqeer, Baloch, Shadi Khan, Alotaibi, Hammad, Khan, Ilyas, Khan, Afrasyab
Format: Article
Language:English
Published: Elsevier Ltd 2021
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access: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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spelling my.utm.953582022-04-29T22:33:11Z http://eprints.utm.my/id/eprint/95358/ A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection Hussain, Saddam Mustafa, Mohd. Wazir A. Jumani, Touqeer Baloch, Shadi Khan Alotaibi, Hammad Khan, Ilyas Khan, Afrasyab TK Electrical engineering. Electronics Nuclear engineering 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. Elsevier Ltd 2021-11 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/95358/1/SaddamHussain2021_ANovelFeatureEngineeredCatBoost.pdf Hussain, Saddam and Mustafa, Mohd. Wazir and A. Jumani, Touqeer and Baloch, Shadi Khan and Alotaibi, Hammad and Khan, Ilyas and Khan, Afrasyab (2021) A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection. Energy Reports, 7 . pp. 4425-4436. ISSN 2352-4847 http://dx.doi.org/10.1016/j.egyr.2021.07.008 DOI:10.1016/j.egyr.2021.07.008
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Hussain, Saddam
Mustafa, Mohd. Wazir
A. Jumani, Touqeer
Baloch, Shadi Khan
Alotaibi, Hammad
Khan, Ilyas
Khan, Afrasyab
A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection
description 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.
format Article
author Hussain, Saddam
Mustafa, Mohd. Wazir
A. Jumani, Touqeer
Baloch, Shadi Khan
Alotaibi, Hammad
Khan, Ilyas
Khan, Afrasyab
author_facet Hussain, Saddam
Mustafa, Mohd. Wazir
A. Jumani, Touqeer
Baloch, Shadi Khan
Alotaibi, Hammad
Khan, Ilyas
Khan, Afrasyab
author_sort Hussain, Saddam
title A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection
title_short A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection
title_full A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection
title_fullStr A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection
title_full_unstemmed A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection
title_sort novel feature engineered-catboost-based supervised machine learning framework for electricity theft detection
publisher Elsevier Ltd
publishDate 2021
url 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
_version_ 1732945463377657856
score 13.211869

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