Science Explorer Back to mapMap
Discussion
Topic · Artificial Intelligence

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. 9.9% 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%
1.9k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+53%
the tick is no change
Largest producer
China
36% of the 10 largest, 2022-now
Machine Learning and Data Classification

Which countries lead Machine Learning and Data Classification research?

By volume, China and the United States publish the most (1.9k and 1.2k works in 2022-now).

By volume, 2022-now

  1. 1 China 1.9k works
  2. 2 United States 1.2k works
  3. 3 India 759 works
  4. 4 Germany 347 works
  5. 5 United Kingdom 247 works
  6. 6 South Korea 178 works
  7. 7 Japan 175 works
  8. 8 France 174 works
  9. 9 Canada 160 works
  10. 10 Italy 158 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.

China: 36.0%United States: 22.4%India: 14.3%Germany: 6.6%6 others listed: 20.6%36%largest
China1,909 · 36.0%United States1,189 · 22.4%India759 · 14.3%Germany347 · 6.6%6 others listed1,093 · 20.6%

Shares of the rows listed above, not of the whole node.

Which institutions lead Machine Learning and Data Classification research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Machine Learning and Data Classification research, followed by Centre National de la Recherche Scientifique and Tsinghua University.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 65 works
  2. 2 Centre National de la Recherche ScientifiqueFrance 53 works
  3. 3 Tsinghua UniversityChina 41 works
  4. 4 Zhejiang UniversityChina 34 works
  5. 5 Nanjing UniversityChina 33 works
  6. 6 Xidian UniversityChina 31 works
  7. 7 National University of Defense TechnologyChina 30 works
  8. 8 Shanghai Jiao Tong UniversityChina 27 works
  9. 9 University of Electronic Science and Technology of ChinaChina 26 works
  10. 10 Microsoft (United States)United States 25 works

Who are the leading researchers in Machine Learning and Data Classification?

The most-cited researchers publishing on Machine Learning and Data Classification include Kaiming He, Yoshua Bengio and Geoffrey E. Hinton.

  1. 1 Kaiming He Israel 562k citations
  2. 2 Yoshua Bengio Canada 476k citations
  3. 3 Geoffrey E. Hinton Canada 454k citations
  4. 4 Robert John Tibshirani United States 396k citations
  5. 5 Steven L. Salzberg United States 372k citations
  6. 6 Ross Girshick Israel 356k citations
  7. 7 Xiangyu Zhang 327k citations
  8. 8 Trevor Hastie United States 301k citations
  9. 9 Jian Sun 281k citations
  10. 10 Charles M. Perou United States 263k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

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).

Most cited papers
#PaperReleasedCitations
1 Random Forests Leo Breiman · Machine Learning Open access Oct 2001 132,956
2 XGBoost Tianqi Chen, Carlos Guestrin Conference paperOpen access 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 Conference paperOpen access 2024 13,571
All 98 most cited papers, with filters and sorting

Where is Machine Learning and Data Classification research done?

The largest centres of Machine Learning and Data Classification research in 2022-now are Beijing (China), Nanjing (China), Shanghai (China) and Xi'an (China).

Largest cities, 2022-now

  1. 1 BeijingChina 338 works
  2. 2 NanjingChina 137 works
  3. 3 ShanghaiChina 125 works
  4. 4 Xi'anChina 105 works
  5. 5 SeoulSouth Korea 101 works
  6. 6 ShenzhenChina 85 works
  7. 7 GuangzhouChina 78 works
  8. 8 HangzhouChina 78 works
  9. 9 WuhanChina 76 works
  10. 10 TokyoJapan 74 works
See Machine Learning and Data Classification on the map

Where is the best place to study Machine Learning and Data Classification?

Among universities, judged by research, Hong Kong University of Science and Technology, Tsinghua University and Zhejiang 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.

0%10%20%30%mean 17.77%fractional works in this field (log) →share of work in the world top 10% →Hong Kong University…Tsinghua UniversityZhejiang UniversityXidian University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Hong Kong University of Science and TechnologyChina 73.823.2%5.1×14 +98.4%
2Tsinghua UniversityChina 72.317.2%2.8×41 +21.9%
3Zhejiang UniversityChina 68.316.7%2.0×34 +235.7%
4Xidian UniversityChina 66.013.9%6.6×31 +329.3%
5Northwestern Polytechnical UniversityChina 65.616.7%3.2×24 +113.8%
6Nanjing University of Information Science and TechnologyChina 64.627.8%4.0×10 +178.3%
7Nanyang Technological UniversitySingapore 64.012.5%3.9×20 +50.5%
8University of Hong KongChina 61.322.1%1.8×11 +215.0%
9University of Electronic Science and Technology of ChinaChina 61.113.0%3.7×26 +106.4%
10Peking UniversityChina 59.814.6%1.8×23 +44.0%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Machine Learning and Data Classification, country by country

Is Machine Learning and Data Classification research growing?

Output in 2022-2025 was 53% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Machine Learning and Data Classification.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Machine Learning and Data Classification as a percentage of every work published that year, 0.030% in 2024.

still counting0.020%0.040%Machine Learning and Data Classification0.034% in 202519801990200020102020

Fractional works of Machine Learning and Data Classification over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Machine Learning and Data Classification9.9% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

Discussion

Ask a question or share a note about Machine Learning and Data Classification. Posts are plain text and can be reported.