Most cited papers in Machine Learning and Data Classification
What are the most cited papers in Machine Learning and Data Classification?
The most cited paper in Machine Learning and Data Classification is "Random Forests" (Leo Breiman, 2001), with 132,956 citations. Next come "XGBoost" (53,418) and "Dropout: a simple way to prevent neural networks from overfitting" (34,210).
Showing 50 of 98 papers. Every paper with more than 1,915 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Random Forests Leo Breiman · Machine Learning | Oct 2001 | 132,956 |
| 2 | XGBoost Tianqi Chen, Carlos Guestrin | 8 Aug 2016 | 53,418 |
| 3 | Dropout: a simple way to prevent neural networks from overfitting Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky et al. | 2014 | 34,210 |
| 4 | A Survey on Transfer Learning Sinno Jialin Pan, Qiang Yang · IEEE Transactions on Knowledge and Data Engineering | 16 Oct 2009 | 23,834 |
| 5 | On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper) Gauriat, Charles-Maxime, Pencolé, Yannick, Ribot, Pauline et al. · OASIcs : OpenAccess Series in Informatics | 2024 | 13,571 |
| 6 | The Elements of Statistical Learning: Data Mining, Inference, and Prediction Zigang Lu · Journal of the Royal Statistical Society Series A (Statistics in Society) | 17 Jun 2010 | 12,808 |
| 7 | A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection Ron Kohavi | 1995 | 10,735 |
| 8 | Object Detection with Discriminatively Trained Part-Based Models Pedro F. Felzenszwalb, Ross Girshick, David McAllester et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 24 Sep 2009 | 10,129 |
| 9 | Machine learning: Trends, perspectives, and prospects Michael I. Jordan, Tom M. Mitchell · Science | 16 Jul 2015 | 10,020 |
| 10 | 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 |
| 11 | Extremely randomized trees Pierre Geurts, Damien Ernst, Louis A. Wehenkel · Machine Learning | 2 Mar 2006 | 9,239 |
| 12 | Wrappers for feature subset selection Ron Kohavi, George H. John · Artificial Intelligence | Dec 1997 | 9,026 |
| 13 | Learning long-term dependencies with gradient descent is difficult Yoshua Bengio, P. Simard, Paolo Frasconi · IEEE Transactions on Neural Networks | Mar 1994 | 8,685 |
| 14 | Optuna Takuya Akiba, Shotaro Sano, Toshihiko Yanase et al. | 25 Jul 2019 | 8,344 |
| 15 | Ensemble Methods in Machine Learning Thomas G. Dietterich · Lecture notes in computer science | 2000 | 8,132 |
| 16 | Random search for hyper-parameter optimization James Bergstra, Yoshua Bengio, Léon Bottou | 2012 | 7,930 |
| 17 | A Unified Approach to Interpreting Model Predictions Scott Lundberg, Su‐In Lee · arXiv (Cornell University) | 22 May 2017 | 7,625 |
| 18 | Experiments with a new boosting algorithm Yoav Freund, Robert E. Schapire | 3 Jul 1996 | 7,566 |
| 19 | 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 |
| 20 | 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 |
| 21 | The random subspace method for constructing decision forests Tin Kam Ho · IEEE Transactions on Pattern Analysis and Machine Intelligence | 1998 | 6,940 |
| 22 | LIBLINEAR: A Library for Large Linear Classification Rong-En Fan, Kai‐Wei Chang, Cho‐Jui Hsieh et al. | Jun 2008 | 6,609 |
| 23 | Multitask Learning Rich Caruana · Machine Learning | Jul 1997 | 6,547 |
| 24 | A survey of transfer learning Karl R. Weiss, Taghi M. Khoshgoftaar, Dingding Wang · Journal Of Big Data | 27 May 2016 | 6,327 |
| 25 | Estimating the Support of a High-Dimensional Distribution Bernhard Schölkopf, John C. Platt, John S. Shawe-Taylor et al. · Neural Computation | Jul 2001 | 6,166 |
| 26 | Top 10 algorithms in data mining Xindong Wu, Vipin Kumar, J. R. Quinlan et al. · Knowledge and Information Systems | 3 Dec 2007 | 5,723 |
| 27 | Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek, Hugo Larochelle, Ryan Prescott Adams · arXiv (Cornell University) | 13 Jun 2012 | 5,667 |
| 28 | Combining labeled and unlabeled data with co-training Avrim L. Blum, Tom M. Mitchell | 24 Jul 1998 | 5,638 |
| 29 | “Why Should I Trust You?”: Explaining the Predictions of Any Classifier Marco Ribeiro, Sameer Kumar Singh, Carlos Guestrin | 2016 | 5,530 |
| 30 | Random decision forests Tin Kam Ho | 19 Nov 2002 | 5,158 |
| 31 | 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 |
| 32 | Supervised Machine Learning: A Review of Classification Techniques Sotiris B. Kotsiantis | 2007 | 4,147 |
| 33 | Instance-Based Learning Algorithms David W. Aha, Dennis F. Kibler, Marc K. Albert · Machine Learning | 1991 | 4,133 |
| 34 | A random forest guided tour Gérard Biau, Erwan Scornet · Test | 19 Apr 2016 | 4,080 |
| 35 | Fast Effective Rule Induction William W. Cohen · Elsevier eBooks | 1995 | 3,813 |
| 36 | Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms Thomas G. Dietterich · Neural Computation | Oct 1998 | 3,800 |
| 37 | Learning with Local and Global Consistency Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal et al. · MPG.PuRe (Max Planck Society) | 9 Dec 2003 | 3,736 |
| 38 | A theory of learning from different domains Shai Ben-David, John C. Blitzer, Koby Crammer et al. · Machine Learning | 22 Oct 2009 | 3,645 |
| 39 | Neural Networks and the Bias/Variance Dilemma Stuart Geman, Elie Bienenstock, René Doursat · Neural Computation | 1992 | 3,629 |
| 40 | BRIEF: Binary Robust Independent Elementary Features Michael Calonder, Vincent Lepetit, Christoph Strecha et al. · Lecture notes in computer science | 2010 | 3,617 |
| 41 | Support Vector Data Description David M. J. Tax, Robert P. W. Duin · Machine Learning | 16 Dec 2003 | 3,551 |
| 42 | An introduction to kernel-based learning algorithms K. Robert Müller, S. Mika, Gunnar Rätsch et al. · IEEE Transactions on Neural Networks | Mar 2001 | 3,499 |
| 43 | Bootstrap your own latent: A new approach to self-supervised Learning Jean-Bastien Grill, Florian Strub, Florent Altché et al. · arXiv (Cornell University) | 13 Jun 2020 | 3,441 |
| 44 | A Survey of Outlier Detection Methodologies Joanna Hodge, Jim Austin · Artificial Intelligence Review | Oct 2004 | 3,405 |
| 45 | Semi-supervised learning using Gaussian fields and harmonic functions Xiaojin Zhu, Zoubin Ghahramani, John Lafferty | 21 Aug 2003 | 3,382 |
| 46 | A few useful things to know about machine learning Pedro Domingos · Communications of the ACM | 25 Sep 2012 | 3,368 |
| 47 | The class imbalance problem: A systematic study1 Nathalie Japkowicz, Shaju Stephen · Intelligent Data Analysis | 15 Nov 2002 | 3,367 |
| 48 | Selection of relevant features and examples in machine learning Avrim L. Blum, Pat Langley · Artificial Intelligence | Dec 1997 | 3,312 |
| 49 | On hyperparameter optimization of machine learning algorithms: Theory and practice Li Yang, Abdallah A. Shami · Neurocomputing | 25 Jul 2020 | 3,270 |
| 50 | Algorithms for Hyper-Parameter Optimization James Bergstra, R. Bardenet, Yoshua Bengio et al. | 2011 | 3,182 |
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.