Machine Learning and Data Classification
Machine Learning and Data Classification is a research topic within Artificial Intelligence. Science Explorer counts 19k research works in it since 1953. 23.3% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the challenges and techniques for learning with noisy labels in machine learning, including methods for hyperparameter optimization, instance selection, robust learning, and automated machine learning. It also explores the use of meta-learning and deep neural networks in handling noisy label problems, particularly in the context of classification tasks and learning from positive and unlabeled data.
- Noisy Labels
- Hyperparameter Optimization
- Instance Selection
- Robust Learning
- Automated Machine Learning
- Meta-Learning
- Deep Neural Networks
- Classification
- Positive and Unlabeled Data
- Loss Correction
- Research works
- 19k fractional, since 1953
- In the world top 10%
- 4.4k per year above
- Top-10% rate
- 23.3% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +121% the tick is no change
Which countries lead Machine Learning and Data Classification research?
By volume, China and the United States publish the most (1.5k and 1k works in 2022–2025).
By volume, 2022–2025
- 1 China 1.5k works
- 2 United States 1k works
- 3 India 631 works
- 4 Germany 317 works
- 5 United Kingdom 219 works
- 6 France 161 works
- 7 Japan 158 works
- 8 South Korea 156 works
- 9 Italy 143 works
- 10 Canada 138 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Machine Learning and Data Classification research?
By volume in 2022–2025, Tsinghua University publishes the most Machine Learning and Data Classification research, followed by Nanjing University and Zhejiang University.
By volume, 2022–2025
- 1 Tsinghua University China 36 works
- 2 Nanjing University China 29 works
- 3 Zhejiang University China 27 works
- 4 Xidian University China 27 works
- 5 National University of Defense Technology China 25 works
- 6 University of Electronic Science and Technology of China China 24 works
- 7 Chinese Academy of Sciences China 23 works
- 8 Harbin Institute of Technology China 21 works
- 9 Shanghai Jiao Tong University China 21 works
- 10 Northwestern Polytechnical University China 20 works
Who are the leading researchers in Machine Learning and Data Classification?
The most-cited researchers publishing on Machine Learning and Data Classification include Yoshua Bengio, Robert Tibshirani and Deva Ramanan.
- 1 Yoshua Bengio 17k citations
- 2 Robert Tibshirani 13k citations
- 3 Deva Ramanan 11k citations
- 4 Wei Liu 9.7k citations
- 5 Chih‐Jen Lin 9.6k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Machine Learning and Data Classification research done?
The largest centres of Machine Learning and Data Classification research in 2022–2025 are Beijing (China), Nanjing (China), Shanghai (China) and Xi'an (China).
Where is the best place to study Machine Learning and Data Classification?
Among universities, judged by research, Victoria University of Wellington, Nanjing University and Florida Atlantic University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Victoria University of Wellington New Zealand | 69.1 | 40.8% | 9.0× | 11 | +145.8% |
| 2 | Nanjing University China | 58.7 | 22.8% | 6.1× | 29 | +159.0% |
| 3 | Florida Atlantic University United States | 56.7 | 46.4% | 8.7× | 10 | -32.3% |
| 4 | Hong Kong University of Science and Technology Hong Kong | 52.1 | 40.9% | 4.6× | 11 | -2.0% |
| 5 | Tsinghua University China | 51.9 | 28.2% | 3.0× | 36 | +77.1% |
| 6 | Carnegie Mellon University United States | 51.2 | 23.8% | 7.3× | 18 | +52.1% |
| 7 | University of Technology Sydney Australia | 50.1 | 28.0% | 5.0× | 14 | +87.1% |
| 8 | Xidian University China | 49.9 | 24.4% | 5.8× | 27 | +40.2% |
| 9 | Ludwig-Maximilians-Universität München Germany | 49.8 | 39.0% | 2.4× | 9 | +220.2% |
| 10 | Nanjing University of Science and Technology China | 48.4 | 29.2% | 3.1× | 16 | +239.5% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Machine Learning and Data Classification research growing?
Output in 2018–2022 was 121% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Machine Learning and Data Classification.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.