Machine Learning Approaches for Imbalanced Domains: Emerging Trends and Applications

Dear Colleagues,

In many real-world domains, the data distribution is highly imbalanced since instances of some classes appear much more frequently than others. This poses a difficulty for machine learning algorithms as they tend to be biased towards the majority class. At the same time, the minority class is typically the most important from a data mining perspective as it may carry valuable knowledge.

Despite more than two decades of continuous research, several open issues remain in the field of imbalance learning, and recent trends increasingly focus on the interaction between class imbalance and other difficulties embedded in the nature of the data, such as the fast-growing data volume and dimensionality, the variability of concepts in time, or the presence of noise and data quality issues. New real-world problems continue to emerge that motivate researchers to focus on advanced learning strategies, which can involve data-level and algorithm-level approaches, to effectively deal with imbalanced datasets.

The aim of this Special Issue is to bring together contributions that discuss problems and solutions in this area, both from a methodological and an application-oriented perspective. Topics of interest include but are not limited to:

• Data-level, algorithm-level, and hybrid approaches;
• Machine learning, ensemble learning, and deep learning methods;
• Multi-label and multi-class imbalanced learning;
• Learning strategies for high-dimensional imbalanced data;
• Learning strategies for imbalanced data streams;
• Learning strategies for imbalanced visual data;
• Noise robustness of learning methods in imbalanced settings;
• Metrics and methodologies for model evaluation in imbalanced settings;
• Real-world applications: industrial monitoring systems, fraud detection, intrusion detection, software defect prediction, medical diagnosis, object detection and image classification, computer vision, text mining, sentiment analysis, anomaly detection, and behavior analysis in social media.

Dr. Barbara Pes
Dr. Andrea Loddo
Guest Editors

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