The problem of imbalanced domain learning has been thoroughly studied in the last two decades, with a specific focus on classification tasks. However, the research community has started to address this problem in other contexts such as regression, ordinal classification, multi-label and multi-class classification, association rules mining, multi-instance learning, data streams, time-series and spatio-temporal forecasting, text mining and multimodal data. Clearly, the research community recognises that imbalanced domains are a broad and important problem. Such a context poses important challenges for both supervised and unsupervised learning tasks, in an increasing number of real-world applications.
Tackling the issues raised by imbalanced domains is crucial to both academia and industry. To researchers, it is an opportunity to develop more adaptable and robust systems/approaches for very complex tasks. These tasks are, in many cases, those that industry is already facing today. These are very diverse and include the ability to prevent fraud, to anticipate catastrophes, and in general to enable a more preemptive action in an increasingly fast-paced world.
This workshop proposal focuses on providing a significant contribution to the problems of learning with imbalanced domains, aiming to increase the interest and the contributions to solving its challenges. The workshop invites inter-disciplinary contributions to tackle the problems that many real-world domains face nowadays. With the growing attention that this challenge has collected, it is crucial to promote its further development in order to tackle its theoretical and application challenges.
The research topics of interest to LIDTA'2023 workshop include (but are not limited to) the following:
All accepted papers will be included in the workshop proceedings, published as a volume in Proceedings of Machine Learning Research (PMLR).