SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
Alberto Fernández, Salvador García, Francisco Herrera, Nitesh V. Chawla
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages - from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reflect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.
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- 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 DataLegacy 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…→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…→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…→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…→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
- SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Ann…→Multiobjective evolutionary algorithms: A survey of the state of the artLegacy 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…→Introduction to Semi-Supervised LearningLegacy 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…→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…→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…→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…→Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced\n Datasets in Machin…Legacy 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→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→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→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→A study of the behavior of several methods for balancing machine learning training dataLegacy edge — source detail unavailable
- Editorial→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
- A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→SMOTEBoost: Improving Prediction of the Minority Class in BoostingLegacy edge — source detail unavailable
- A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→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…→Learning from Imbalanced DataLegacy 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
- 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→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→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→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→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→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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…→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…→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…→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…→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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 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
- Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine …→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
- Learning from class-imbalanced data: Review of methods and applications→Introduction to Semi-Supervised LearningLegacy 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→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→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
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