The class imbalance problem: A systematic study1

Nathalie Japkowicz; Shaju Stephen

doi:https://doi.org/10.3233/ida-2002-6504

The class imbalance problem: A systematic study1

2002 · Intelligent Data Analysis · 3,353 citations · 15 from inside this corpus

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.

17 of 17 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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Learning from Imbalanced Data→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…→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→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Ensemble learning: A survey→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
A systematic study of the class imbalance problem in convolutional neural networks→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 class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Exploratory Undersampling for Class-Imbalance Learning→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Learning from imbalanced data: open challenges and future directions→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Editorial→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
The Balanced Accuracy and Its Posterior Distribution→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
CLASSIFICATION OF IMBALANCED DATA: A REVIEW→The class imbalance problem: A systematic study1Legacy 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
Machine learning: a review of classification and combining techniques→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Cost-sensitive boosting for classification of imbalanced data→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
Mining with rarity→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
The class imbalance problem: A systematic study1→The foundations of cost-sensitive learningLegacy edge — source detail unavailable
The class imbalance problem: A systematic study1→UCI Repository of Machine Learning DatabasesLegacy edge — source detail unavailable
Mining with rarity→The foundations of cost-sensitive learningLegacy edge — source detail unavailable
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Cl…→The foundations of cost-sensitive learningLegacy 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
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
Editorial→The foundations of cost-sensitive learningLegacy 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
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…→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
Cost-sensitive boosting for classification of imbalanced data→The foundations of cost-sensitive learningLegacy 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→UCI Repository of Machine Learning DatabasesLegacy edge — source detail unavailable
Exploratory Undersampling for Class-Imbalance Learning→The foundations of cost-sensitive learningLegacy 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
Learning from Imbalanced Data→The foundations of cost-sensitive learningLegacy 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
An insight into classification with imbalanced data: Empirical results and current trends…→The foundations of cost-sensitive learningLegacy 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…→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…→Learning from Imbalanced DataLegacy 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→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
A systematic study of the class imbalance problem in convolutional neural networks→The foundations of cost-sensitive learningLegacy 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
Ensemble learning: A survey→A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…Legacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
The foundations of cost-sensitive learning	2001	1,837
UCI Repository of Machine Learning Databases	1994	1,812

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
A study of the behavior of several methods for balancing machine learning training data	2004	4,222
Ensemble learning: A survey	2018	3,088
A systematic study of the class imbalance problem in convolutional neural networks	2018	3,072
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
Editorial	2004	2,081
The Balanced Accuracy and Its Posterior Distribution	2010	1,728
CLASSIFICATION OF IMBALANCED DATA: A REVIEW	2009	1,713
An insight into classification with imbalanced data: Empirical results and current trends…	2013	1,602
Machine learning: a review of classification and combining techniques	2006	1,562
Cost-sensitive boosting for classification of imbalanced data	2007	1,438
Mining with rarity	2004	1,396

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Topics

Imbalanced Data Classification Techniques	Computer Science
Machine Learning and Data Classification	Computer Science
Machine Learning and Algorithms	Computer Science

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supports2 author record(s) attached.
supports18 reference(s) recorded.
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