[CfP] 5th International Workshop on Learning with Imbalanced Domains (with ECML 2023)

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Nuno Moniz

May 23, 2023, 10:29:06 PMMay 23
to Machine Learning News
*Apologies for multi-posting*


LIDTA 2023, co-located with ECML/PKDD 2023
5th International Workshop on Learning with Imbalanced Domains: Theory and Applications
18-22 September, Turin, Italy





Submission Deadline: Wednesday, June 21, 2023
Notification of Acceptance: Wednesday, July 12, 2023
Camera-ready Deadline: Friday, July 26, 2023

ECML/PKDD 2023: September 18-22, 2023
LIDTA 2023: September 18, 2023


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'2021 workshop include (but are not limited to) the following:

*** Foundations of learning in imbalanced domains
Probabilistic and statistical models
New knowledge discovery theories and models
Probabilistic and statistical models
New knowledge discovery theories and models
Deep learning
Handling imbalanced big data
One-class learning
Learning with non i.i.d. data
Rare event detection in classification tasks
New approaches for data pre-processing (e.g. resampling strategies)
Post-processing approaches
Sampling approaches
Feature selection and feature transformation
Evaluation metrics and methodologies
Ensemble methods
Instance hardness

*** Knowledge discovery and machine learning in imbalanced domains
Classification, ordinal classification
Data streams and time series forecasting
Adaptive learning and algorithm-level approaches
Multi-label, multi-instance, sequence and association rules mining
Active learning
Spatial and spatio-temporal learning
Text and image mining
Multi-modal learning
Predictive Maintenance
Automated machine learning

*** Applications in imbalanced domains
Health applications (e.g. medical imaging)
Fraud detection (e.g. finance, credit and online banking)
Anomaly detection (e.g. industry, intrusion detection, privacy and security)
Environmental applications (e.g. meteorology, biology, oil spill detection)
Social media applications (e.g. popularity prediction, recommender systems)
Fake news detection and disinformation, deep fake classification
Other real world applications and case studies



- For each accepted paper, a presentation slot of 15 minutes is provided.
- The maximum length for papers is 14 pages. Papers not respecting such limit will be rejected.
- All submissions must be written in English and follow the PMLR format.
- All submissions will be reviewed by the Program Committee using a double-blind method. As such, it is required that no personal information or reference to the authors should be introduced in the submitted paper.
- Papers that have already been accepted or are currently under review for other workshops, conferences, or journals will not be considered.
- Submissions will be evaluated concerning their technical quality, relevance, significance, originality and clarity.
- At least one author of each accepted paper must attend the workshop and present the paper.



All accepted papers will be included in the workshop proceedings, published as a volume in Proceedings of Machine Learning Research (PMLR).






Nuno Moniz | Lucy Family Institute for Data & Society, University of Notre Dame, USA
Paula Branco | University of Ottawa, Canada
Luís Torgo | Dalhousie University, Canada
Nathalie Japkowicz | American University, USA
Michał Woźniak | Wroclaw University of Science and Technology, Poland
Shuo Wang | University of Birmingham, UK
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