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Imbalanced Data Classification Techniques
TopicLeading institutions, researchers & key papers
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.
97
Works
IDs:OpenAlex
How has Imbalanced Data Classification Techniques's publication output changed over time?
ScholarIQpublication output · 2008–2024
Output grew0% over the shown period — from 1 works in 2008 to 1 in 2024.
1
1
2
1
1
2
2
3
1
1
2008201320162018201920202021202220232024
What are the most-cited papers on Imbalanced Data Classification Techniques?
ScholarIQmost cited works
Facing Imbalanced Data--Recommendations for the Use of Performance Metrics
László A. Jeni, Jeffrey F. Cohn, Fernando De la Torre
2013811 Citations
A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining
Tarid Wongvorachan, Surina He, Okan Bulut
S4210219776. 2023354 CitationsOPEN ACCESS
Credit Card Fraud Detection - Machine Learning methods
Dejan Varmedja, Mirjana Karanovic, Srdjan Sladojević, Marko Arsenović, Andraš Anderla
2019318 Citations
Intelligent financial fraud detection practices in post-pandemic era
Xiaoqian Zhu, Xiang Ao, Zidi Qin, Yanpeng Chang, Yang Liu, Qing He, Jianping Li
S4210236180. 2021190 CitationsOPEN ACCESS
A survey on statistical methods for health care fraud detection
Jing Li, Kuei-Ying Huang, Jionghua Jin, Jianjun Shi
S59475652. 2008172 Citations
Where is Imbalanced Data Classification Techniques research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
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How much of the research on Imbalanced Data Classification Techniques is open access?
ScholarIQopen access share
53%OPEN ACCESS
Gold
33%
Green
0%
Hybrid
13%
Bronze
7%
Closed
47%
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A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining
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Credit Card Fraud Detection - Machine Learning methods
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