v241: Proceedings of LIDTA 2023

2026-06-10 · Source: Proceedings of Machine Learning Research · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

Volume 241 presents the proceedings of the Fifth International Workshop on Learning with Imbalanced Domains: Theory and Applications, held on September 18, 2023, at ECML-PKDD in Turin, Italy. Edited by Nuno Moniz, Paula Branco, Luis Torgo, Nathalie Japkowicz, Michal Wozniak, and Shuo Wang, this volume features four research papers addressing critical challenges in handling imbalanced datasets. Topics explored include deep similarity learning loss functions for data transformation, the impact of balancing methods on model behavior, and performance estimation bias in class imbalance, particularly with minority subconcepts. Additionally, one paper introduces FSDA for tackling tail-event analysis in imbalanced time series data through feature selection and data augmentation.

Key takeaway

This workshop addresses the critical challenge of class imbalance in machine learning, offering novel techniques and exposing key pitfalls for robust model development. It introduces deep similarity learning loss functions, analyzes balancing method impacts, and proposes FSDA for imbalanced time series, alongside revealing significant performance estimation biases from minority subconcepts. These insights are crucial for ML practitioners, data scientists, and researchers seeking to build fair, accurate models and reliably evaluate performance in real-world skewed data scenarios.

Topics

• Learning with Imbalanced Domains
• Class Imbalance
• Deep Similarity Learning
• Balancing Methods
• Performance Estimation

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.