Next-Generation Imbalanced Learning: Trustworthiness, Scalability, and Representation: A Special Issue Dedicated to the Memory of Prof. Barbara Pes

Dear Colleagues,

In the era of Big Data, class imbalance has shifted from a specific edge case to a pervasive characteristic of real-world environments. While the minority class often holds the highest analytical value, representing critical events like system failures, rare diseases, or security breaches, standard machine learning algorithms remain inherently biased toward the majority.

However, the challenge is no longer just about skewness. Modern imbalance learning must now operate at the intersection of massive dimensionality, distributed data silos, and the urgent need for transparency. We are moving beyond traditional resampling methods toward advanced strategies that integrate robust feature engineering (selection, extraction, and reduction) to tackle the "curse of dimensionality."

Furthermore, as AI deployment scales in sensitive sectors, "black box" solutions are no longer acceptable. The cutting-edge of research now focuses on Explainable AI (XAI) to ensure that minority-class predictions are interpretable and Federated Learning to handle imbalanced data across decentralized networks without compromising privacy.

The aim of this Special Issue is to chart the path forward, gathering cutting-edge contributions that bridge the gap between theoretical novelty and industrial applicability.

We invite papers addressing the following key themes:

1. Advanced Representation and Feature Engineering:

• Novel strategies for dimensionality reduction and manifold learning in imbalanced spaces.
• Feature selection and extraction techniques that are tailored for highly skewed datasets.
• Representation learning and embeddings for minority class enhancement.

2. Trustworthy and Distributed AI:

• Explainability (XAI): Interpretability mechanisms for models trained on imbalanced data.
• Federated Learning: Handling non-IID and imbalanced data in privacy-preserving, distributed environments.
• Fairness-aware learning and bias mitigation.

3. Architectures and Methodologies:

• Deep Learning and Ensemble architectures for complex imbalance.
• Hybrid data-level and algorithm-level strategies.
• Learning from imbalanced data streams and concept drift adaptation.
• Multi-label, multi-class, and noisy-label learning scenarios.

4. Applications:

• Industrial IoT monitoring.
• Fraud and intrusion detection.
• Radiomics and medical diagnostics.
• Software defect prediction.
• Social media behavior analysis.

We dedicate this Special Issue to the memory of Prof. Barbara Pes, whose pioneering work on high-dimensional and imbalanced data has inspired much of the current research in this area. Her contributions — notably on combining feature selection with cost-sensitive and ensemble methods to address class imbalance in high-dimensional biomedical datasets, set a strong foundation for the directions we pursue in this special issue.

Dr. Andrea Loddo
Guest Editor

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