Most cited papers in Electricity Theft Detection Techniques
What are the most cited papers in Electricity Theft Detection Techniques?
The most cited paper in Electricity Theft Detection Techniques is "Learning from Imbalanced Data" (Haibo He, Edwardo A. Garcia, 2009), with 10,349 citations. Next come "A systematic study of the class imbalance problem in convolutional neural networks" (3,120) and "Survey on deep learning with class imbalance" (3,018).
Showing 50 of 99 papers. Every paper with more than 318 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Learning from Imbalanced Data Haibo He, Edwardo A. Garcia · IEEE Transactions on Knowledge and Data Engineering | Jul 2009 | 10,349 |
| 2 | A systematic study of the class imbalance problem in convolutional neural networks Mateusz Buda, Atsuto Maki, Maciej A. Mazurowski · Neural Networks | 29 Jul 2018 | 3,120 |
| 3 | Survey on deep learning with class imbalance Justin M. Johnson, Taghi M. Khoshgoftaar · Journal Of Big Data | 19 Mar 2019 | 3,018 |
| 4 | A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches Mikel Galar, Alberto Fernández, Edurne Barrenechea et al. · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) | 15 Aug 2011 | 2,851 |
| 5 | Exploratory Undersampling for Class-Imbalance Learning Xuying Liu, Jianxin Wu, Zhihua Zhou · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) | 18 Dec 2008 | 2,514 |
| 6 | Learning from imbalanced data: open challenges and future directions Bartosz Krawczyk · Progress in Artificial Intelligence | 22 Apr 2016 | 2,511 |
| 7 | Learning from class-imbalanced data: Review of methods and applications Guo Haixiang, Yijing Li, Jennifer S. Shang et al. · Expert Systems with Applications | 29 Dec 2016 | 2,431 |
| 8 | SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary Alberto Fernández, Salvador García, Francisco Herrera et al. · Journal of Artificial Intelligence Research | 20 Apr 2018 | 2,243 |
| 9 | RUSBoost: A Hybrid Approach to Alleviating Class Imbalance Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse et al. · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans | 3 Nov 2009 | 1,902 |
| 10 | CLASSIFICATION OF IMBALANCED DATA: A REVIEW Yanmin Sun, Andrew K. C. Wong, Mohamed S. Kamel · International Journal of Pattern Recognition and Artificial Intelligence | Jun 2009 | 1,725 |
| 11 | An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics Victoria López, Alberto Fernández, Salvador García et al. · Information Sciences | 12 Jul 2013 | 1,613 |
| 12 | Cost-sensitive boosting for classification of imbalanced data Yanmin Sun, Mohamed S. Kamel, Andrew K. C. Wong et al. · Pattern Recognition | 6 May 2007 | 1,445 |
| 13 | Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges Yi Wang, Qixin Chen, Tao Hong et al. · IEEE Transactions on Smart Grid | 22 Mar 2018 | 1,355 |
| 14 | The 2015 Ukraine Blackout: Implications for False Data Injection Attacks Gaoqi Liang, Steven R. Weller, Junhua Zhao et al. · IEEE Transactions on Power Systems | 22 Nov 2016 | 1,252 |
| 15 | Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE Georgios Douzas, Fernando Bação, Felix Last · Information Sciences | 28 Jun 2018 | 1,229 |
| 16 | Security and Privacy Challenges in the Smart Grid Patrick Drew McDaniel, Stephen McLaughlin · IEEE Security & Privacy | May 2009 | 1,178 |
| 17 | MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning Sukarna Barua, Md. Monirul Islam, Xin Yao et al. · IEEE Transactions on Knowledge and Data Engineering | 26 Nov 2012 | 1,173 |
| 18 | Data Mining for Imbalanced Datasets: An Overview Nitesh V. Chawla | 2009 | 1,091 |
| 19 | A Multiple Resampling Method for Learning from Imbalanced Data Sets Andrew Estabrooks, Taeho Jo, Nathalie Japkowicz · Computational Intelligence | 28 Jan 2004 | 1,031 |
| 20 | SVMs Modeling for Highly Imbalanced Classification Yuchun Tang, Yanqing Zhang, Nitesh V. Chawla et al. · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) | 10 Dec 2008 | 983 |
| 21 | Data imbalance in classification: Experimental evaluation Fadi Thabtah, Suhel Hammoud, Firuz Kamalov et al. · Information Sciences | 11 Nov 2019 | 864 |
| 22 | Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism Youbiao He, Gihan J. Mendis, Jin Wei · IEEE Transactions on Smart Grid | 15 May 2017 | 840 |
| 23 | Experimental perspectives on learning from imbalanced data Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano | 20 Jun 2007 | 798 |
| 24 | Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids Zibin Zheng, Yatao Yang, Xiangdong Niu et al. · IEEE Transactions on Industrial Informatics | 21 Dec 2017 | 797 |
| 25 | A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance Dina Elreedy, Amir F. Atiya · Information Sciences | 19 Jul 2019 | 783 |
| 26 | A survey on addressing high-class imbalance in big data Joffrey L. Leevy, Taghi M. Khoshgoftaar, Richard A. Bauder et al. · Journal Of Big Data | Nov 2018 | 778 |
| 27 | Electricity Theft Detection in AMI Using Customers’ Consumption Patterns Paria Jokar, Nasim Arianpoo, Victor C. M. Leung · IEEE Transactions on Smart Grid | 14 May 2015 | 762 |
| 28 | Class imbalances versus small disjuncts Taeho Jo, Nathalie Japkowicz · ACM SIGKDD Explorations Newsletter | Jun 2004 | 687 |
| 29 | Calibrating Probability with Undersampling for Unbalanced Classification Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson et al. | Dec 2015 | 657 |
| 30 | Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives Yassine Himeur, Khalida Ghanem, Abdullah Alsalemi et al. · Applied Energy | 9 Feb 2021 | 651 |
| 31 | A survey on Advanced Metering Infrastructure Ramyar Rashed Mohassel, Alan S. Fung, Farah A. Mohammadi et al. · International Journal of Electrical Power & Energy Systems | 7 Jul 2014 | 638 |
| 32 | Smart Grid Privacy via Anonymization of Smart Metering Data Costas Efthymiou, Georgios Kalogridis | Oct 2010 | 614 |
| 33 | Learning from imbalanced data sets with boosting and data generation Hongyu Guo, Herna Lydia Viktor · ACM SIGKDD Explorations Newsletter | Jun 2004 | 610 |
| 34 | Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances Waleed Hilal, S. Andrew Gadsden, John Yawney · Expert Systems with Applications | 31 Dec 2021 | 600 |
| 35 | Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey Damminda Alahakoon, Xinghuo Yu · IEEE Transactions on Industrial Informatics | 18 Mar 2015 | 598 |
| 36 | Borderline over-sampling for imbalanced data classification Hien M. Nguyen, Eric Wallace Cooper, Katsuari Kamei · International Journal of Knowledge Engineering and Soft Data Paradigms | 2011 | 597 |
| 37 | Multiclass Imbalance Problems: Analysis and Potential Solutions Shuo Wang, Xin Yao · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) | 19 Mar 2012 | 577 |
| 38 | Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions Tianci Zhang, Jinglong Chen, Fudong Li et al. · ISA Transactions | 8 Mar 2021 | 571 |
| 39 | Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures Nikos Komninos, Eleni Philippou, Andreas Pitsillides · IEEE Communications Surveys & Tutorials | 2014 | 565 |
| 40 | Diversity analysis on imbalanced data sets by using ensemble models Shuo Wang, Xin Yao | Mar 2009 | 553 |
| 41 | Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid Anish Jindal, Amit Dua, Kuljeet Kaur et al. · IEEE Transactions on Industrial Informatics | 16 Mar 2016 | 547 |
| 42 | A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation Azal Ahmad Khan, Omkar Chaudhari, Rohitash Chandra · Expert Systems with Applications | 9 Dec 2023 | 528 |
| 43 | Motor Anomaly Detection for Unmanned Aerial Vehicles Using Reinforcement Learning Huimin Lu, Yujie Li, Shenglin Mu et al. · IEEE Internet of Things Journal | 8 Aug 2017 | 507 |
| 44 | A Systematic Review on Imbalanced Data Challenges in Machine Learning Harsurinder Kaur, Husanbir Singh Pannu, Avleen Kaur Malhi · ACM Computing Surveys | 30 Aug 2019 | 500 |
| 45 | SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory Enislay Ramentol, Yailé Caballero Mota, Rafael Bello et al. · Knowledge and Information Systems | 3 Dec 2011 | 494 |
| 46 | Overview and performance assessment of the clustering methods for electrical load pattern grouping Gianfranco Chicco · Energy | 29 Jan 2012 | 490 |
| 47 | Cyber security of a power grid: State-of-the-art Chih‐Che Sun, Adam Hahn, Chen‐Ching Liu · International Journal of Electrical Power & Energy Systems | 4 Jan 2018 | 486 |
| 48 | A survey on imbalanced learning: latest research, applications and future directions Wuxing Chen, Kaixiang Yang, Zhiwen Yu et al. · Artificial Intelligence Review | 9 May 2024 | 484 |
| 49 | Combining unsupervised and supervised learning in credit card fraud detection Fabrizio Carcillo, Yann‐Aël Le Borgne, Olivier Caelen et al. · Information Sciences | 15 May 2019 | 482 |
| 50 | Parrot optimizer: Algorithm and applications to medical problems Junbo Jacob Lian, Guohua Hui, Ling Ma et al. · Computers in Biology and Medicine | 24 Feb 2024 | 475 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.