RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

Obtaining outstanding electricity theft detection (ETD) performance in the realm of advanced metering infrastructure (AMI) and smart grids (SGs) is quite difficult due to various issues. The issues include limited availability of theft data as compared to benign data, neglecting dimensionality reduc...

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Detalhes bibliográficos

Principais autores:	Pamir, Nadeem Javaid, Ahmad Almogren, Muhammad Adil, Muhammad Umar Javed, Mansour Zuair
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2022-01-01
coleção:	IEEE Access
Assuntos:	K nearest neighbor oversampling approach bidirectional long short term memory LogitBoosting deep learning machine learning stacking ensemble model
Acesso em linha:	https://ieeexplore.ieee.org/document/9923755/
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