An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics

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• 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→An insight into classification with imbalanced data: Empirical results and current trends…Legacy edge — source detail unavailable
• Classification assessment methods→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…→An insight into classification with imbalanced data: Empirical results and current trends…Legacy 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
• An insight into classification with imbalanced data: Empirical results and current trends…→Support-vector networksLegacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→Support-Vector NetworksLegacy 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…→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→Modern Information RetrievalLegacy 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…→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…→Proceedings of the 25th international conference on Machine learning - ICML '08Legacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→The relationship between Precision-Recall and ROC curvesLegacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→Advances in kernel methods: support vector 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…→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…→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 class imbalance problem: A systematic study1Legacy 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
• 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…→Exploratory Undersampling for Class-Imbalance LearningLegacy 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…→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…→The foundations of cost-sensitive learningLegacy 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…→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…→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…→Cost-sensitive boosting for classification of imbalanced dataLegacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→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
• The class imbalance problem: A systematic study1→The foundations of cost-sensitive 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 foundations of cost-sensitive learningLegacy 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
• Mining with rarity→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• The relationship between Precision-Recall and ROC curves→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy 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→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
• CLASSIFICATION OF IMBALANCED DATA: A REVIEW→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 foundations of cost-sensitive learningLegacy 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
• 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→The foundations of cost-sensitive learningLegacy 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→The relationship between Precision-Recall and ROC curvesLegacy 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→Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets LearningLegacy edge — source detail unavailable
• RUSBoost: A Hybrid Approach to Alleviating 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…→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…→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy 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…→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…→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…→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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…→Ensemble based systems in decision makingLegacy 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 foundations of cost-sensitive learningLegacy 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→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
• 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 foundations of cost-sensitive learningLegacy 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→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→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
• 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→The foundations of cost-sensitive learningLegacy edge — source detail unavailable
• ADASYN: Adaptive synthetic sampling approach for imbalanced learning→Mining with rarityLegacy edge — source detail unavailable
• ADASYN: Adaptive synthetic sampling approach for imbalanced learning→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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 foundations of cost-sensitive learningLegacy edge — source detail unavailable
• Learning from Imbalanced Data→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
• Learning from Imbalanced Data→Mining with rarityLegacy edge — source detail unavailable
• Learning from Imbalanced Data→The relationship between Precision-Recall and ROC curvesLegacy 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
• Learning from Imbalanced Data→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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→C4.5: Programs for Machine LearningLegacy 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
• 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
• 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
• 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
• 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→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→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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
• 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…→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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
• Classification assessment methods→The relationship between Precision-Recall and ROC curvesLegacy edge — source detail unavailable
• Classification assessment methods→Learning from Imbalanced DataLegacy edge — source detail unavailable
• Classification assessment methods→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable