Most cited papers in Imbalanced Data Classification Techniques
What are the most cited papers in Imbalanced Data Classification Techniques?
The most cited paper in Imbalanced Data Classification Techniques is "SMOTE: Synthetic Minority Over-sampling Technique" (Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall et al., 2002), with 32,759 citations. Next come "An introduction to ROC analysis" (22,014) and "Mining association rules between sets of items in large databases" (14,855).
Showing 50 of 96 papers. Every paper with more than 1,233 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall et al. · Journal of Artificial Intelligence Research | Jun 2002 | 32,759 |
| 2 | An introduction to ROC analysis Tom E. Fawcett · Pattern Recognition Letters | 22 Dec 2005 | 22,014 |
| 3 | Mining association rules between sets of items in large databases Rakesh Agrawal, Tomasz Imieliński, Arun N. Swami | Jun 1993 | 14,855 |
| 4 | pROC: an open-source package for R and S+ to analyze and compare ROC curves Xavier Robin, Natacha Turck, Alexandre Hainard et al. · BMC Bioinformatics | 17 Mar 2011 | 14,550 |
| 5 | Learning from Imbalanced Data Haibo He, Edwardo A. Garcia · IEEE Transactions on Knowledge and Data Engineering | Jul 2009 | 10,349 |
| 6 | LightGBM: A Highly Efficient Gradient Boosting Decision Tree Guolin Ke, Qi Meng, Thomas Finley et al. · HAL (Le Centre pour la Communication Scientifique Directe) | 4 Dec 2017 | 9,485 |
| 7 | Wrappers for feature subset selection Ron Kohavi, George H. John · Artificial Intelligence | Dec 1997 | 9,026 |
| 8 | Experiments with a new boosting algorithm Yoav Freund, Robert E. Schapire | 3 Jul 1996 | 7,566 |
| 9 | The use of the area under the ROC curve in the evaluation of machine learning algorithms Andrew P. Bradley · Pattern Recognition | Jul 1997 | 7,349 |
| 10 | Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors) Jerome H. Friedman, Trevor Hastie, Robert John Tibshirani · The Annals of Statistics | Apr 2000 | 6,998 |
| 11 | The relationship between Precision-Recall and ROC curves Jesse J. Davis, Mark Goadrich | 2006 | 6,494 |
| 12 | The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation Davide Chicco, Giuseppe Jurman · BMC Genomics | 2 Jan 2020 | 6,216 |
| 13 | Basic principles of ROC analysis Charles E. Metz · Seminars in Nuclear Medicine | Oct 1978 | 6,107 |
| 14 | The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets Takaya Saito, Marc Rehmsmeier · PLoS ONE | 4 Mar 2015 | 5,231 |
| 15 | ADASYN: Adaptive synthetic sampling approach for imbalanced learning Haibo He, Yang Bai, Edwardo A. Garcia et al. | Jun 2008 | 4,590 |
| 16 | A study of the behavior of several methods for balancing machine learning training data Gustavo E. A. P. A. Batista, Ronaldo Cristiano Prati, Maria Carolina Monard · ACM SIGKDD Explorations Newsletter | Jun 2004 | 4,251 |
| 17 | Supervised Machine Learning: A Review of Classification Techniques Sotiris B. Kotsiantis | 2007 | 4,147 |
| 18 | Instance-Based Learning Algorithms David W. Aha, Dennis F. Kibler, Marc K. Albert · Machine Learning | 1991 | 4,133 |
| 19 | Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning Hui Han, Wenyuan Wang, Binghuan Mao · Lecture notes in computer science | 2005 | 4,075 |
| 20 | Fast Effective Rule Induction William W. Cohen · Elsevier eBooks | 1995 | 3,813 |
| 21 | A Survey of Outlier Detection Methodologies Joanna Hodge, Jim Austin · Artificial Intelligence Review | Oct 2004 | 3,405 |
| 22 | ROCR: visualizing classifier performance in R Tobias Sing, Oliver Sander, Niko Beerenwinkel et al. · Computer applications in the biosciences | 11 Aug 2005 | 3,374 |
| 23 | The class imbalance problem: A systematic study1 Nathalie Japkowicz, Shaju Stephen · Intelligent Data Analysis | 15 Nov 2002 | 3,367 |
| 24 | The Strength of Weak Learnability Robert E. Schapire · Machine Learning | Jun 1990 | 3,356 |
| 25 | Mining frequent patterns without candidate generation Jiawei Han, Jian Pei, Yiwen Yin | 16 May 2000 | 3,228 |
| 26 | 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 |
| 27 | On the Optimality of the Simple Bayesian Classifier under Zero-One Loss Pedro Domingos, Michael J. Pazzani · Machine Learning | Nov 1997 | 3,111 |
| 28 | Survey on deep learning with class imbalance Justin M. Johnson, Taghi M. Khoshgoftaar · Journal Of Big Data | 19 Mar 2019 | 3,018 |
| 29 | Popular Ensemble Methods: An Empirical Study David W. Opitz, Richard Maclin · Journal of Artificial Intelligence Research | Aug 1999 | 2,997 |
| 30 | An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization Thomas G. Dietterich · Machine Learning | Aug 2000 | 2,969 |
| 31 | 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 |
| 32 | Solving Multiclass Learning Problems via Error-Correcting Output Codes Tom Dietterich, Ghulum Bakiri · Journal of Artificial Intelligence Research | 1995 | 2,735 |
| 33 | Mining sequential patterns: Generalizations and performance improvements Ramakrishnan Srikant, Rakesh Agrawal · Lecture notes in computer science | 1996 | 2,716 |
| 34 | An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants Eric J. Bauer, Ron Kohavi · Machine Learning | Jul 1999 | 2,668 |
| 35 | Improved boosting algorithms using confidence-rated predictions Robert E. Schapire, Yoram Singer | 24 Jul 1998 | 2,569 |
| 36 | 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 |
| 37 | Learning from imbalanced data: open challenges and future directions Bartosz Krawczyk · Progress in Artificial Intelligence | 22 Apr 2016 | 2,511 |
| 38 | 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 |
| 39 | Classification assessment methods Alaa Tharwat · Applied Computing and Informatics | 21 Aug 2018 | 2,388 |
| 40 | A comprehensive survey on support vector machine classification: Applications, challenges and trends Jair Cervantes, Farid García‐Lamont, Lisbeth Rodríguez-Mazahua et al. · Neurocomputing | 8 May 2020 | 2,377 |
| 41 | Boosting the margin: a new explanation for the effectiveness of voting methods Peter L. Bartlett, Yoav Freund, Wee Sun Lee et al. · The Annals of Statistics | Oct 1998 | 2,345 |
| 42 | Explaining prediction models and individual predictions with feature contributions Erik Štrumbelj, Igor Kononenko · Knowledge and Information Systems | 29 Aug 2013 | 2,336 |
| 43 | Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy Ludmila Ilieva Kuncheva, Christopher J. Whitaker · Machine Learning | 4 Apr 2003 | 2,332 |
| 44 | Bias in error estimation when using cross-validation for model selection Sudhir Varma, Richard M Simon · BMC Bioinformatics | 23 Feb 2006 | 2,329 |
| 45 | A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems David J. Hand, Robert John Till · Machine Learning | Nov 2001 | 2,311 |
| 46 | 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 |
| 47 | Using AUC and accuracy in evaluating learning algorithms Jin Huang, Charles X. Ling · IEEE Transactions on Knowledge and Data Engineering | 31 Jan 2005 | 2,173 |
| 48 | Reliability in Content Analysis. Klaus Krippendorff · Human Communication Research | Jul 2004 | 2,148 |
| 49 | KNN Model-Based Approach in Classification Gongde Guo, Hui Wang, David Bell et al. · Lecture notes in computer science | 2003 | 2,121 |
| 50 | Analysis of Representations for Domain Adaptation Shai Ben-David, John C. Blitzer, Koby Crammer et al. · The MIT Press eBooks | 7 Sep 2007 | 2,053 |
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.