This paper discusses the challenges and future directions in the field of learning from imbalanced data, a topic that has received significant attention over the past two decades. Despite advancements, several open issues remain, particularly in areas such as classification, regression, clustering, data streams, and big data analytics. The paper identifies seven key areas of research: 1. **Binary Imbalanced Classification**: Challenges include analyzing the structure of classes, handling extreme class imbalance, adjusting classifier outputs, and ensemble learning. 2. **Multi-Class Imbalanced Classification**: Issues include data preprocessing, multi-class decomposition, and designing skew-insensitive multi-class classifiers. 3. **Multi-Label and Multi-Instance Imbalanced Classification**: Challenges involve developing skew-insensitive methods, using decomposition strategies, and handling uncertainty in bags and instances. 4. **Regression in Imbalanced Scenarios**: Open issues include cost-sensitive regression, distinguishing between minority and noisy samples, and ensemble learning. 5. **Semi-Supervised and Unsupervised Learning from Imbalanced Data**: Challenges include adjusting clustering methods, detecting class imbalance in semi-supervised and active learning, and handling drifting data streams. 6. **Learning from Imbalanced Data Streams**: Issues include handling new class emergence, class label availability, and adapting to recurring drifts. 7. **Imbalanced Big Data**: Challenges include scalable and efficient algorithms for handling heterogeneous and atypical data, interpretable analysis, and processing complex data structures. The paper emphasizes the need for further research to address these challenges and improve the understanding and handling of imbalanced data in various machine learning applications.This paper discusses the challenges and future directions in the field of learning from imbalanced data, a topic that has received significant attention over the past two decades. Despite advancements, several open issues remain, particularly in areas such as classification, regression, clustering, data streams, and big data analytics. The paper identifies seven key areas of research: 1. **Binary Imbalanced Classification**: Challenges include analyzing the structure of classes, handling extreme class imbalance, adjusting classifier outputs, and ensemble learning. 2. **Multi-Class Imbalanced Classification**: Issues include data preprocessing, multi-class decomposition, and designing skew-insensitive multi-class classifiers. 3. **Multi-Label and Multi-Instance Imbalanced Classification**: Challenges involve developing skew-insensitive methods, using decomposition strategies, and handling uncertainty in bags and instances. 4. **Regression in Imbalanced Scenarios**: Open issues include cost-sensitive regression, distinguishing between minority and noisy samples, and ensemble learning. 5. **Semi-Supervised and Unsupervised Learning from Imbalanced Data**: Challenges include adjusting clustering methods, detecting class imbalance in semi-supervised and active learning, and handling drifting data streams. 6. **Learning from Imbalanced Data Streams**: Issues include handling new class emergence, class label availability, and adapting to recurring drifts. 7. **Imbalanced Big Data**: Challenges include scalable and efficient algorithms for handling heterogeneous and atypical data, interpretable analysis, and processing complex data structures. The paper emphasizes the need for further research to address these challenges and improve the understanding and handling of imbalanced data in various machine learning applications.