SMOTE: Synthetic Minority Over-sampling Technique

Nitesh V. Chawla; Kevin W. Bowyer; Lawrence Hall; W. Philip Kegelmeyer

doi:https://doi.org/10.1613/jair.953

SMOTE: Synthetic Minority Over-sampling Technique

2002 · Journal of Artificial Intelligence Research · 32,402 citations · 25 from inside this corpus

Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall, W. Philip Kegelmeyer low

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

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Learning from Imbalanced Data→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
mixup: Beyond Empirical Risk Minimization→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
ADASYN: Adaptive synthetic sampling approach for imbalanced learning→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
A systematic study of the class imbalance problem in convolutional neural networks→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
Survey on deep learning with class imbalance→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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
Exploratory Undersampling for Class-Imbalance Learning→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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 class-imbalanced data: Review of methods and applications→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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
Editorial→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
CatBoost for big data: an interdisciplinary review→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
The Balanced Accuracy and Its Posterior Distribution→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
AutoML: A survey of the state-of-the-art→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
Text Data Augmentation for Deep Learning→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
SMOTEBoost: Improving Prediction of the Minority Class in Boosting→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine …→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
Mining with rarity→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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→UCI Repository of machine learning databasesLegacy 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→Fast Effective Rule InductionLegacy edge — source detail unavailable
SMOTE: Synthetic Minority Over-sampling Technique→Pattern ClassificationLegacy edge — source detail unavailable
SMOTE: Synthetic Minority Over-sampling Technique→Inductive learning algorithms and representations for text categorizationLegacy edge — source detail unavailable
SMOTEBoost: Improving Prediction of the Minority Class in Boosting→Fast Effective Rule InductionLegacy edge — source detail unavailable
SMOTEBoost: Improving Prediction of the Minority Class in Boosting→UCI Repository of machine learning databasesLegacy edge — source detail unavailable
SMOTEBoost: Improving Prediction of the Minority Class in Boosting→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
Fast Effective Rule Induction→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→Fast Effective Rule InductionLegacy edge — source detail unavailable
Mining with rarity→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
Mining with rarity→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…→EditorialLegacy edge — source detail unavailable
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…→Cost-sensitive boosting for classification of imbalanced dataLegacy edge — source detail unavailable
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…→Learning from Imbalanced DataLegacy edge — source detail unavailable
A study of the behavior of several methods for balancing machine learning training data→UCI Repository of machine learning databasesLegacy 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
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→Mining with rarityLegacy 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→EditorialLegacy 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
Editorial→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy 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
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→Fast Effective Rule InductionLegacy edge — source detail unavailable
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance→Mining with rarityLegacy 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→UCI Repository of machine learning databasesLegacy 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→Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets LearningLegacy 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…→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…→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy 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…→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…→Exploratory Undersampling for Class-Imbalance LearningLegacy 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…→C4.5: Programs for Machine LearningLegacy 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…→Pattern ClassificationLegacy 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→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→EditorialLegacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→C4.5: Programs for Machine LearningLegacy 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→Mining with rarityLegacy 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→EditorialLegacy edge — source detail unavailable
Exploratory Undersampling for Class-Imbalance Learning→UCI Repository of machine learning databasesLegacy 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
ADASYN: Adaptive synthetic sampling approach for imbalanced learning→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
ADASYN: Adaptive synthetic sampling approach for imbalanced learning→Mining with rarityLegacy 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→Mining with rarityLegacy 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→EditorialLegacy 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→ADASYN: Adaptive synthetic sampling approach for imbalanced learningLegacy edge — source detail unavailable
Learning from Imbalanced Data→Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets LearningLegacy edge — source detail unavailable
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→Mining with rarityLegacy edge — source detail unavailable
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→A study of the behavior of several methods for balancing machine learning training dataLegacy edge — source detail unavailable
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→EditorialLegacy edge — source detail unavailable
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→UCI Repository of machine learning databasesLegacy edge — source detail unavailable
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy 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
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…→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…→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy 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…→RUSBoost: A Hybrid Approach to Alleviating Class ImbalanceLegacy 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…→Exploratory Undersampling for Class-Imbalance LearningLegacy edge — source detail unavailable
An insight into classification with imbalanced data: Empirical results and current trends…→ADASYN: Adaptive synthetic sampling approach for imbalanced 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…→Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets LearningLegacy 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
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→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→ADASYN: Adaptive synthetic sampling approach for imbalanced learningLegacy 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→An insight into classification with imbalanced data: Empirical results and current trends…Legacy edge — source detail unavailable
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine …→Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets LearningLegacy 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→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy 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→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→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→An insight into classification with imbalanced data: Empirical results and current trends…Legacy edge — source detail unavailable
A systematic study of the class imbalance problem in convolutional neural networks→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
A systematic study of the class imbalance problem in convolutional neural networks→Exploratory Undersampling for Class-Imbalance LearningLegacy edge — source detail unavailable
A systematic study of the class imbalance problem in convolutional neural networks→Learning from Imbalanced DataLegacy edge — source detail unavailable
A systematic study of the class imbalance problem in convolutional neural networks→Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets LearningLegacy edge — source detail unavailable
A systematic study of the class imbalance problem in convolutional neural networks→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
A systematic study of the class imbalance problem in convolutional neural networks→Learning from class-imbalanced data: Review of methods and applicationsLegacy 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…→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…→ADASYN: Adaptive synthetic sampling approach for imbalanced learningLegacy 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…→Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets LearningLegacy 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…→Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine …Legacy 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
Survey on deep learning with class imbalance→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
Survey on deep learning with class imbalance→Mining with rarityLegacy edge — source detail unavailable
Survey on deep learning with class imbalance→EditorialLegacy edge — source detail unavailable
Survey on deep learning with class imbalance→Cost-sensitive boosting for classification of imbalanced dataLegacy edge — source detail unavailable
Survey on deep learning with class imbalance→Exploratory Undersampling for Class-Imbalance LearningLegacy edge — source detail unavailable
Survey on deep learning with class imbalance→Learning from Imbalanced DataLegacy edge — source detail unavailable
Survey on deep learning with class imbalance→Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets LearningLegacy edge — source detail unavailable
Survey on deep learning with class imbalance→Learning from imbalanced data: open challenges and future directionsLegacy edge — source detail unavailable
Survey on deep learning with class imbalance→A systematic study of the class imbalance problem in convolutional neural networksLegacy edge — source detail unavailable
AutoML: A survey of the state-of-the-art→mixup: Beyond Empirical Risk MinimizationLegacy edge — source detail unavailable
CatBoost for big data: an interdisciplinary review→The Balanced Accuracy and Its Posterior DistributionLegacy edge — source detail unavailable
CatBoost for big data: an interdisciplinary review→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
CatBoost for big data: an interdisciplinary review→Survey on deep learning with class imbalanceLegacy edge — source detail unavailable
Text Data Augmentation for Deep Learning→RUSBoost: A Hybrid Approach to Alleviating Class ImbalanceLegacy edge — source detail unavailable
Text Data Augmentation for Deep Learning→Survey on deep learning with class imbalanceLegacy edge — source detail unavailable
Text Data Augmentation for Deep Learning→AutoML: A survey of the state-of-the-artLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
C4.5: Programs for Machine Learning	1992	23,704
UCI Repository of machine learning databases	1998	10,546
The use of the area under the ROC curve in the evaluation of machine learning algorithms	1997	7,328
Fast Effective Rule Induction	1995	3,810
Pattern Classification	2001	3,744
Inductive learning algorithms and representations for text categorization	1998	1,469

What cites it, inside the corpus

Paper	Year	Cited
Learning from Imbalanced Data	2009	10,209
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…	2015	5,051
mixup: Beyond Empirical Risk Minimization	2017	4,806
ADASYN: Adaptive synthetic sampling approach for imbalanced learning	2008	4,556
A study of the behavior of several methods for balancing machine learning training data	2004	4,222
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning	2005	4,049
A systematic study of the class imbalance problem in convolutional neural networks	2018	3,072
Survey on deep learning with class imbalance	2019	2,971
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…	2011	2,834
Exploratory Undersampling for Class-Imbalance Learning	2008	2,504
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
Editorial	2004	2,081
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance	2009	1,890
CatBoost for big data: an interdisciplinary review	2020	1,776
The Balanced Accuracy and Its Posterior Distribution	2010	1,728
CLASSIFICATION OF IMBALANCED DATA: A REVIEW	2009	1,713
AutoML: A survey of the state-of-the-art	2020	1,705
Text Data Augmentation for Deep Learning	2021	1,701
SMOTEBoost: Improving Prediction of the Minority Class in Boosting	2003	1,680
An insight into classification with imbalanced data: Empirical results and current trends…	2013	1,602
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine …	2016	1,561
Cost-sensitive boosting for classification of imbalanced data	2007	1,438
Mining with rarity	2004	1,396

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Imbalanced Data Classification Techniques	Computer Science
Anomaly Detection Techniques and Applications	Computer Science
Text and Document Classification Technologies	Computer Science

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