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Imbalanced Data Classification Techniques

Imbalanced Data Classification Techniques is a research topic within Artificial Intelligence. Science Explorer counts 21k research works in it since 1954. 10.0% 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 handling imbalanced data in classification problems. It covers methods such as SMOTE, ROC analysis, cost-sensitive learning, ensemble methods, and their applications in fraud detection. The cluster also discusses the use of precision-recall and boosting algorithms, as well as the effectiveness of random forest in addressing imbalanced datasets.

  • Imbalanced Data
  • Classification
  • SMOTE
  • ROC Analysis
  • Cost-Sensitive Learning
  • Ensemble Methods
  • Fraud Detection
  • Precision-Recall
  • Boosting
  • Random Forest
Research works
21k
fractional, since 1954
In the world top 10%
2.1k
per year above
Top-10% rate
10.0%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+75%
the tick is no change
Largest producer
China
31% of the 10 largest, 2022-now
Imbalanced Data Classification Techniques

Which countries lead Imbalanced Data Classification Techniques research?

By volume, China and India publish the most (2k and 2k works in 2022-now).

By volume, 2022-now

  1. 1 China 2k works
  2. 2 India 2k works
  3. 3 United States 968 works
  4. 4 Indonesia 464 works
  5. 5 Türkiye 239 works
  6. 6 United Kingdom 211 works
  7. 7 Brazil 211 works
  8. 8 Bangladesh 153 works
  9. 9 ?? 143 works
  10. 10 Malaysia 139 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: 30.8%India: 30.4%United States: 14.8%Indonesia: 7.1%6 others listed: 16.8%31%largest
China2,008 · 30.8%India1,985 · 30.4%United States968 · 14.8%Indonesia464 · 7.1%6 others listed1,096 · 16.8%

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

Which institutions lead Imbalanced Data Classification Techniques research?

By volume in 2022-now, Saveetha University publishes the most Imbalanced Data Classification Techniques research, followed by SRM Institute of Science and Technology and Vellore Institute of Technology University.

By volume, 2022-now

  1. 1 Saveetha UniversityIndia 104 works
  2. 2 SRM Institute of Science and TechnologyIndia 67 works
  3. 3 Vellore Institute of Technology UniversityIndia 45 works
  4. 4 Binus UniversityIndonesia 44 works
  5. 5 Chandigarh UniversityIndia 43 works
  6. 6 Chinese Academy of SciencesChina 42 works
  7. 7 Amrita Vishwa VidyapeethamIndia 34 works
  8. 8 Manipal Academy of Higher EducationIndia 29 works
  9. 9 Koneru Lakshmaiah Education FoundationIndia 28 works
  10. 10 Symbiosis International UniversityIndia 27 works

Who are the leading researchers in Imbalanced Data Classification Techniques?

The most-cited researchers publishing on Imbalanced Data Classification Techniques include Yoshua Bengio, Geoffrey E. Hinton and Robert John Tibshirani.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Geoffrey E. Hinton Canada 454k citations
  3. 3 Robert John Tibshirani United States 396k citations
  4. 4 Steven L. Salzberg United States 372k citations
  5. 5 Gregory Y.H. Lip United Kingdom 311k citations
  6. 6 Donald B. Rubin United States 301k citations
  7. 7 Trevor Hastie United States 301k citations
  8. 8 Farshad Farzadfar Iran 298k citations
  9. 9 Charles M. Perou United States 263k citations
  10. 10 Ralph B. D’Agostino United States 255k 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 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).

Most cited papers
#PaperReleasedCitations
1 SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall et al. · Journal of Artificial Intelligence Research Open access 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 Conference paperOpen access 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 Open access 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
All 96 most cited papers, with filters and sorting

Where is Imbalanced Data Classification Techniques research done?

The largest centres of Imbalanced Data Classification Techniques research in 2022-now are Beijing (China), Chennai (India), Shanghai (China) and Bengaluru (India). Among places with at least 20 works in it, it is an unusually large share of all research in Boca Raton, Vijayawada and Greater Noida.

Largest cities, 2022-now

  1. 1 BeijingChina 316 works
  2. 2 ChennaiIndia 283 works
  3. 3 ShanghaiChina 147 works
  4. 4 BengaluruIndia 108 works
  5. 5 ChengduChina 105 works
  6. 6 DhakaBangladesh 102 works
  7. 7 PuneIndia 97 works
  8. 8 NanjingChina 96 works
  9. 9 GuangzhouChina 91 works
  10. 10 Xi'anChina 88 works

Where it is the local speciality

  1. Boca RatonUS · 24.1 works14×
  2. VijayawadaIN · 42.1 works10×
  3. Greater NoidaIN · 51.1 works8.9×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Imbalanced Data Classification Techniques than the world average.

See Imbalanced Data Classification Techniques on the map

Where is the best place to study Imbalanced Data Classification Techniques?

Among universities, judged by research, University of Johannesburg, Hefei University of Technology 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.

0%20%40%mean 20.29%fractional works in this field (log) →share of work in the world top 10% →University of Johann…Hefei University of …Xi'an Jiaotong Unive…Florida Atlantic Uni…
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
1University of JohannesburgSouth Africa 61.134.5%2.0×10 +60.6%
2Hefei University of TechnologyChina 58.731.5%3.0×13 +71.9%
3Florida Atlantic UniversityUnited States 57.420.1%15.9×24 +10.9%
4Xi'an Jiaotong UniversityChina 56.824.8%1.6×22 +51.8%
5Saveetha UniversityIndia 56.42.6%13.8×104 +2415.0%
6University of Technology SydneyAustralia 54.623.8%3.7×13 +50.3%
7King Saud UniversitySaudi Arabia 53.125.7%1.4×11 +76.7%
8South China University of TechnologyChina 52.527.3%2.0×17 +59.6%
9SRM Institute of Science and TechnologyIndia 52.14.6%8.3×67 +262.2%
10Vellore Institute of Technology UniversityIndia 51.38.0%5.8×45 +36.5%

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 Imbalanced Data Classification Techniques, country by country

Is Imbalanced Data Classification Techniques research growing?

Output in 2022-2025 was 75% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Imbalanced Data Classification Techniques.

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 Imbalanced Data Classification Techniques as a percentage of every work published that year, 0.036% in 2024.

still counting0.020%0.040%0.060%Imbalanced Data Classification Techniques0.045% in 202519801990200020102020

Fractional works of Imbalanced Data Classification Techniques 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%Imbalanced Data Classification Techniques9.9% 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.

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