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. 27.8% 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%
- 5.9k per year above
- Top-10% rate
- 27.8% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +109% the tick is no change
Which countries lead Imbalanced Data Classification Techniques research?
By volume, China and India publish the most (1.7k and 1.7k works in 2022–2025).
By volume, 2022–2025
- 1 China 1.7k works
- 2 India 1.7k works
- 3 United States 775 works
- 4 Indonesia 405 works
- 5 Türkiye 211 works
- 6 Brazil 195 works
- 7 United Kingdom 184 works
- 8 Bangladesh 128 works
- 9 ?? 126 works
- 10 Malaysia 122 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 Imbalanced Data Classification Techniques research?
By volume in 2022–2025, 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, 2022–2025
- 1 Saveetha University India 100 works
- 2 SRM Institute of Science and Technology India 45 works
- 3 Vellore Institute of Technology University India 41 works
- 4 Binus University Indonesia 40 works
- 5 Chandigarh University India 39 works
- 6 Amrita Vishwa Vidyapeetham India 31 works
- 7 Koneru Lakshmaiah Education Foundation India 25 works
- 8 Chitkara University India 24 works
- 9 Sepuluh Nopember Institute of Technology Indonesia 23 works
- 10 Symbiosis International University India 23 works
Who are the leading researchers in Imbalanced Data Classification Techniques?
The most-cited researchers publishing on Imbalanced Data Classification Techniques include Philip S. Yu, Francisco Herrera and Dacheng Tao.
- 1 Philip S. Yu 6.6k citations
- 2 Francisco Herrera 6.5k citations
- 3 Dacheng Tao 6.1k citations
- 4 Witold Pedrycz 5.4k citations
- 5 Jiawei Han 5.2k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Imbalanced Data Classification Techniques research done?
The largest centres of Imbalanced Data Classification Techniques research in 2022–2025 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, 2022–2025
Where it is the local speciality
- Boca RatonUS · 22.5 works15×
- VijayawadaIN · 36.4 works10×
- Greater NoidaIN · 42.1 works8.8×
Location quotient: how much more of its research is in Imbalanced Data Classification Techniques than the world average.
Where is the best place to study Imbalanced Data Classification Techniques?
Among universities, judged by research, Koneru Lakshmaiah Education Foundation, Florida Atlantic University and Southwestern University of Finance and Economics 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 | Koneru Lakshmaiah Education Foundation India | 63.0 | 29.5% | 11.4× | 25 | +662.2% |
| 2 | Florida Atlantic University United States | 56.5 | 35.3% | 17.0× | 22 | +59.6% |
| 3 | Southwestern University of Finance and Economics China | 56.4 | 30.5% | 10.8× | 12 | +168.2% |
| 4 | Saveetha University India | 56.0 | 8.2% | 13.6× | 100 | — |
| 5 | National Institute of Technology Raipur India | 55.4 | 43.5% | 8.5× | 9 | +190.9% |
| 6 | SRM University India | 53.5 | 45.4% | 9.1× | 13 | — |
| 7 | Vellore Institute of Technology University India | 52.9 | 26.6% | 5.5× | 41 | +242.7% |
| 8 | Chandigarh University India | 52.1 | 22.7% | 9.2× | 39 | — |
| 9 | University of the Cumberlands United States | 51.3 | 36.2% | 29.9× | 10 | — |
| 10 | Amrita Vishwa Vidyapeetham India | 50.4 | 33.7% | 7.6× | 31 | — |
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 Imbalanced Data Classification Techniques research growing?
Output in 2018–2022 was 109% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Imbalanced Data Classification Techniques.
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.