Learning from imbalanced data: open challenges and future directions
Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.
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- Survey on deep learning with class imbalance→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 imbalanced data: open challenges and future directionsLegacy 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→Learning from Imbalanced DataLegacy 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→The class imbalance problem: A systematic study1Legacy 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→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy 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
- CLASSIFICATION OF IMBALANCED DATA: A REVIEW→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
- CLASSIFICATION OF IMBALANCED DATA: A REVIEW→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 class imbalance problem: A systematic study1Legacy 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→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
- Learning from Imbalanced Data→The class imbalance problem: A systematic study1Legacy edge — source detail unavailable
- Learning from Imbalanced Data→ADASYN: Adaptive synthetic sampling approach for imbalanced learningLegacy edge — source detail unavailable
- Learning from Imbalanced Data→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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…→CLASSIFICATION OF IMBALANCED DATA: A REVIEWLegacy 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…→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy 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…→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…→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
- Survey on deep learning with class imbalance→Learning from Imbalanced DataLegacy edge — source detail unavailable
- Survey on deep learning with class imbalance→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
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| Imbalanced Data Classification Techniques | Computer Science |
| Electricity Theft Detection Techniques | Engineering |
| Anomaly Detection Techniques and Applications | Computer Science |
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