CLASSIFICATION OF IMBALANCED DATA: A REVIEW

Yanmin Sun; Andrew K. C. Wong; Mohamed S. Kamel

doi:https://doi.org/10.1142/s0218001409007326

CLASSIFICATION OF IMBALANCED DATA: A REVIEW

2009 · International Journal of Pattern Recognition and Artificial Intelligence · 1,713 citations · 5 from inside this corpus

Yanmin Sun, Andrew K. C. Wong, Mohamed S. Kamel

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

22 of 22 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

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Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy edge — source detail unavailable
Learning from imbalanced data: open challenges and future directions→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy edge — source detail unavailable
Learning from class-imbalanced data: Review of methods and applications→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy edge — source detail unavailable
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→Random ForestsLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→Induction of Decision TreesLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→Introduction to the theory of neural computationLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→On combining classifiersLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→Bayesian Network ClassifiersLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→A study of the behavior of several methods for balancing machine learning training dataLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→EditorialLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→The Boosting Approach to Machine Learning: An OverviewLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→Improved Boosting Algorithms Using Confidence-rated PredictionsLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→Introduction to the Theory of Neural ComputationLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→Cost-sensitive boosting for classification of imbalanced dataLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→Mining with rarityLegacy edge — source detail unavailable
SMOTEBoost: Improving Prediction of the Minority Class in Boosting→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
SMOTEBoost: Improving Prediction of the Minority Class in Boosting→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
Bayesian Network Classifiers→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
Mining with rarity→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
Mining with rarity→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Mining with rarity→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
Mining with rarity→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
A study of the behavior of several methods for balancing machine learning training data→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
A study of the behavior of several methods for balancing machine learning training data→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
A study of the behavior of several methods for balancing machine learning training data→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
Editorial→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
Editorial→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Editorial→Mining with rarityLegacy edge — source detail unavailable
Editorial→A study of the behavior of several methods for balancing machine learning training dataLegacy edge — source detail unavailable
Editorial→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
Editorial→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→A study of the behavior of several methods for balancing machine learning training dataLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Cost-sensitive boosting for classification of imbalanced dataLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→On combining classifiersLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Improved Boosting Algorithms Using Confidence-rated PredictionsLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→Bayesian Network ClassifiersLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→Mining with rarityLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→A study of the behavior of several methods for balancing machine learning training dataLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→EditorialLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→Induction of Decision TreesLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→On combining classifiersLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→The Boosting Approach to Machine Learning: An OverviewLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→Random ForestsLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→Improved Boosting Algorithms Using Confidence-rated PredictionsLegacy edge — source detail unavailable
SMOTE: Synthetic Minority Over-sampling Technique→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→Mining with rarityLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→A study of the behavior of several methods for balancing machine learning training dataLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→EditorialLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…Legacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→Cost-sensitive boosting for classification of imbalanced dataLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
The Boosting Approach to Machine Learning: An Overview→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
The Boosting Approach to Machine Learning: An Overview→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
The Boosting Approach to Machine Learning: An Overview→Improved Boosting Algorithms Using Confidence-rated PredictionsLegacy edge — source detail unavailable
Learning from imbalanced data: open challenges and future directions→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Learning from imbalanced data: open challenges and future directions→A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…Legacy edge — source detail unavailable
Learning from imbalanced data: open challenges and future directions→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
Learning from imbalanced data: open challenges and future directions→An insight into classification with imbalanced data: Empirical results and current trends…Legacy edge — source detail unavailable
Learning from class-imbalanced data: Review of methods and applications→Mining with rarityLegacy edge — source detail unavailable
Learning from class-imbalanced data: Review of methods and applications→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
Learning from class-imbalanced data: Review of methods and applications→A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…Legacy edge — source detail unavailable
Learning from class-imbalanced data: Review of methods and applications→Cost-sensitive boosting for classification of imbalanced dataLegacy edge — source detail unavailable
Learning from class-imbalanced data: Review of methods and applications→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
Learning from class-imbalanced data: Review of methods and applications→An insight into classification with imbalanced data: Empirical results and current trends…Legacy edge — source detail unavailable
Learning from class-imbalanced data: Review of methods and applications→Improved Boosting Algorithms Using Confidence-rated PredictionsLegacy edge — source detail unavailable
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…→A study of the behavior of several methods for balancing machine learning training dataLegacy edge — source detail unavailable
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…→EditorialLegacy edge — source detail unavailable
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…→A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…Legacy edge — source detail unavailable
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…→An insight into classification with imbalanced data: Empirical results and current trends…Legacy edge — source detail unavailable
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…→Learning from imbalanced data: open challenges and future directionsLegacy edge — source detail unavailable
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…→Learning from class-imbalanced data: Review of methods and applicationsLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Random Forests	2001	131,109
SMOTE: Synthetic Minority Over-sampling Technique	2002	32,402
C4.5: Programs for Machine Learning	1992	23,704
Induction of Decision Trees	1986	15,020
Introduction to the theory of neural computation	1994	6,461
On combining classifiers	1998	5,302
Bayesian Network Classifiers	1997	4,754
A study of the behavior of several methods for balancing machine learning training data	2004	4,222
The class imbalance problem: A systematic study1	2002	3,353
Improved boosting algorithms using confidence-rated predictions	1998	2,573
Editorial	2004	2,081
The Boosting Approach to Machine Learning: An Overview	2003	2,023
Improved Boosting Algorithms Using Confidence-rated Predictions	1999	1,966
Introduction to the Theory of Neural Computation	2018	1,789
SMOTEBoost: Improving Prediction of the Minority Class in Boosting	2003	1,680
Cost-sensitive boosting for classification of imbalanced data	2007	1,438
Mining with rarity	2004	1,396

What cites it, inside the corpus

Paper	Year	Cited
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…	2011	2,834
Learning from imbalanced data: open challenges and future directions	2016	2,489
Learning from class-imbalanced data: Review of methods and applications	2016	2,407
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…	2018	2,226
An insight into classification with imbalanced data: Empirical results and current trends…	2013	1,602

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Topics

Imbalanced Data Classification Techniques	Computer Science
Financial Distress and Bankruptcy Prediction	Business, Management and Accounting
Electricity Theft Detection Techniques	Engineering

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supports82 reference(s) recorded.
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