A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

Mikel Galar; Alberto Fernández; Edurne Barrenechea; Humberto Bustince; Francisco Herrera

doi:https://doi.org/10.1109/tsmcc.2011.2161285

A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

2011 · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · 2,834 citations · 7 from inside this corpus

Mikel Galar, Alberto Fernández, Edurne Barrenechea, Humberto Bustince, Francisco Herrera

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Ensemble learning: A survey→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→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→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…→A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…Legacy edge — source detail unavailable
A survey on ensemble learning→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…→A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…Legacy edge — source detail unavailable
Deep Learning Applications in Medical Image Analysis→A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…Legacy 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…→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Bagging PredictorsLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Bagging predictorsLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Statistical Comparisons of Classifiers over Multiple Data SetsLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Learning from Imbalanced DataLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy 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…→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…→Pattern ClassificationLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Neural Networks and the Bias/Variance DilemmaLegacy 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…→The Strength of Weak LearnabilityLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Combining Pattern ClassifiersLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Ensemble based systems in decision makingLegacy 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…→Exploratory Undersampling for Class-Imbalance LearningLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→The strength of weak learnabilityLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Ac…Legacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Advanced nonparametric tests for multiple comparisons in the design of experiments in com…Legacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Using AUC and accuracy in evaluating learning algorithmsLegacy 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…→RUSBoost: A Hybrid Approach to Alleviating Class ImbalanceLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy 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…→Decision combination in multiple classifier systemsLegacy edge — source detail unavailable
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→KEEL: a software tool to assess evolutionary algorithms for data mining problemsLegacy 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
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
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
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→The class imbalance problem: A systematic study1Legacy 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→Improved boosting algorithms using confidence-rated predictionsLegacy 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→C4.5: Programs for Machine LearningLegacy 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→On combining classifiersLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→Improved Boosting Algorithms Using Confidence-rated PredictionsLegacy edge — source detail unavailable
Using AUC and accuracy in evaluating learning algorithms→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
Using AUC and accuracy in evaluating learning algorithms→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance→A study of the behavior of several methods for balancing machine learning training dataLegacy edge — source detail unavailable
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance→Cost-sensitive boosting for classification of imbalanced dataLegacy edge — source detail unavailable
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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→The class imbalance problem: A systematic study1Legacy 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→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→On combining classifiersLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→Bagging PredictorsLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→Bagging predictorsLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→Improved Boosting Algorithms Using Confidence-rated PredictionsLegacy edge — source detail unavailable
Exploratory Undersampling for Class-Imbalance Learning→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
Exploratory Undersampling for Class-Imbalance Learning→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Exploratory Undersampling for Class-Imbalance Learning→A study of the behavior of several methods for balancing machine learning training dataLegacy edge — source detail unavailable
Exploratory Undersampling for Class-Imbalance Learning→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
Exploratory Undersampling for Class-Imbalance Learning→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
Exploratory Undersampling for Class-Imbalance Learning→Bagging PredictorsLegacy edge — source detail unavailable
Exploratory Undersampling for Class-Imbalance Learning→Bagging predictorsLegacy edge — source detail unavailable
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Ac…→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Ac…→Bagging PredictorsLegacy edge — source detail unavailable
Learning from Imbalanced Data→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
Learning from Imbalanced Data→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Learning from Imbalanced Data→A study of the behavior of several methods for balancing machine learning training dataLegacy edge — source detail unavailable
Learning from Imbalanced Data→Cost-sensitive boosting for classification of imbalanced dataLegacy edge — source detail unavailable
Learning from Imbalanced Data→Exploratory Undersampling for Class-Imbalance LearningLegacy edge — source detail unavailable
Learning from Imbalanced Data→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
Neural Network Ensembles, Cross Validation, and Active Learning→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
KEEL: a software tool to assess evolutionary algorithms for data mining problems→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
SMOTE: Synthetic Minority Over-sampling Technique→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
SMOTE: Synthetic Minority Over-sampling Technique→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
SMOTE: Synthetic Minority Over-sampling Technique→Pattern ClassificationLegacy edge — source detail unavailable
The use of the area under the ROC curve in the evaluation of machine learning algorithms→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
On combining classifiers→Decision combination in multiple classifier systemsLegacy edge — source detail unavailable
On combining classifiers→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
On combining classifiers→Bagging PredictorsLegacy edge — source detail unavailable
On combining classifiers→Bagging predictorsLegacy 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…→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…→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→Using AUC and accuracy in evaluating learning algorithmsLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→RUSBoost: A Hybrid Approach to Alleviating Class ImbalanceLegacy 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…→Exploratory Undersampling for Class-Imbalance LearningLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→Learning from 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…→KEEL: a software tool to assess evolutionary algorithms for data mining problemsLegacy 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
An insight into classification with imbalanced data: Empirical results and current trends…→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→Ensemble based systems in decision makingLegacy edge — source detail unavailable
Advanced nonparametric tests for multiple comparisons in the design of experiments in com…→Statistical Comparisons of Classifiers over Multiple Data SetsLegacy edge — source detail unavailable
Advanced nonparametric tests for multiple comparisons in the design of experiments in com…→KEEL: a software tool to assess evolutionary algorithms for data mining problemsLegacy edge — source detail unavailable
Ensemble based systems in decision making→The Strength of Weak LearnabilityLegacy edge — source detail unavailable
Ensemble based systems in decision making→Decision combination in multiple classifier systemsLegacy edge — source detail unavailable
Ensemble based systems in decision making→Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Ac…Legacy edge — source detail unavailable
Ensemble based systems in decision making→On combining classifiersLegacy edge — source detail unavailable
Ensemble based systems in decision making→Bagging PredictorsLegacy edge — source detail unavailable
Ensemble based systems in decision making→The strength of weak learnabilityLegacy edge — source detail unavailable
Ensemble based systems in decision making→Bagging predictorsLegacy edge — source detail unavailable
Ensemble based systems in decision making→Pattern ClassificationLegacy 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→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy edge — source detail unavailable
Learning from imbalanced data: open challenges and future directions→Learning from Imbalanced DataLegacy 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→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy 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→RUSBoost: A Hybrid Approach to Alleviating Class ImbalanceLegacy 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→Exploratory Undersampling for Class-Imbalance LearningLegacy edge — source detail unavailable
Learning from class-imbalanced data: Review of methods and applications→Learning from 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
Deep Learning Applications in Medical Image Analysis→An insight into classification with imbalanced data: Empirical results and current trends…Legacy edge — source detail unavailable
Ensemble learning: A survey→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Ensemble learning: A survey→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
Ensemble learning: A survey→Ensemble based systems in decision makingLegacy edge — source detail unavailable
Ensemble learning: A survey→Combining Pattern ClassifiersLegacy edge — source detail unavailable
Ensemble learning: A survey→The strength of weak learnabilityLegacy edge — source detail unavailable
Ensemble learning: A survey→Bagging predictorsLegacy 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…→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy edge — source detail unavailable
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…→Learning from Imbalanced DataLegacy 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
A survey on ensemble learning→The Strength of Weak LearnabilityLegacy edge — source detail unavailable
A survey on ensemble learning→Exploratory Undersampling for Class-Imbalance LearningLegacy edge — source detail unavailable
A survey on ensemble learning→Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Ac…Legacy edge — source detail unavailable
A survey on ensemble learning→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
A survey on ensemble learning→Bagging PredictorsLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
SMOTE: Synthetic Minority Over-sampling Technique	2002	32,402
C4.5: Programs for Machine Learning	1992	23,704
Bagging Predictors	1996	17,191
Bagging predictors	1996	16,468
Statistical Comparisons of Classifiers over Multiple Data Sets	2006	11,215
Learning from Imbalanced Data	2009	10,209
The use of the area under the ROC curve in the evaluation of machine learning algorithms	1997	7,328
On combining classifiers	1998	5,302
A study of the behavior of several methods for balancing machine learning training data	2004	4,222
Pattern Classification	2001	3,744
Neural Networks and the Bias/Variance Dilemma	1992	3,612
The class imbalance problem: A systematic study1	2002	3,353
The Strength of Weak Learnability	1990	3,344
Combining Pattern Classifiers	2004	2,980
Ensemble based systems in decision making	2006	2,965
Improved boosting algorithms using confidence-rated predictions	1998	2,573
Exploratory Undersampling for Class-Imbalance Learning	2008	2,504
The strength of weak learnability	1990	2,477
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Ac…	2003	2,321
Advanced nonparametric tests for multiple comparisons in the design of experiments in com…	2009	2,231
Using AUC and accuracy in evaluating learning algorithms	2005	2,163
Improved Boosting Algorithms Using Confidence-rated Predictions	1999	1,966
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance	2009	1,890
Neural Network Ensembles, Cross Validation, and Active Learning	1994	1,803
CLASSIFICATION OF IMBALANCED DATA: A REVIEW	2009	1,713
SMOTEBoost: Improving Prediction of the Minority Class in Boosting	2003	1,680
Decision combination in multiple classifier systems	1994	1,515
KEEL: a software tool to assess evolutionary algorithms for data mining problems	2008	1,508
Cost-sensitive boosting for classification of imbalanced data	2007	1,438

What cites it, inside the corpus

Paper	Year	Cited
Ensemble learning: A survey	2018	3,088
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
A survey on ensemble learning	2019	1,951
An insight into classification with imbalanced data: Empirical results and current trends…	2013	1,602
Deep Learning Applications in Medical Image Analysis	2017	1,497

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Imbalanced Data Classification Techniques	Computer Science
Electricity Theft Detection Techniques	Engineering
Vehicle License Plate Recognition	Engineering

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