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LIDTA 2017 : 1st International Workshop on Learning with Imbalanced Domains: Theory and Applications


When Sep 22, 2017 - Sep 22, 2017
Where Skopje, Macedonia
Submission Deadline Jul 3, 2017
Notification Due Jul 24, 2017
Final Version Due Aug 7, 2017
Categories    machine learning   data mining   imbalanced domains   statistics

Call For Papers

1st International Workshop on Learning with Imbalanced Domains: Theory and Applications (LIDTA 2017, co-located with ECML/PKDD 2017)
22nd September, 2017
Skopje, Macedonia


The proceedings of this workshop will be published as a volume of the Proceedings of Machine Learning Research (PMLR) series.


Submission Deadline: Monday, July 10, 2017
Notification of Acceptance: Monday, July 24, 2017
Camera-ready Deadline: Monday, August 7, 2017
ECML/PKDD 2017: 18-22nd September, 2017
LIDTA 2017: 22th September, 2017 (tentative)


Many real-world data-mining applications involve obtaining and evaluating predictive models using data sets with strongly imbalanced distributions of the target variable. Frequently, the least-common values are associated with events that are highly relevant for end users. This problem has been thoroughly studied in the last decade with a specific focus on classification tasks. However, the research community has started to address this problem within other contexts such as regression, ordinal classification, multi-label classification, multi-instance learning, data streams and time series forecasting. It is now recognized that imbalanced domains are a broader and important problem posing relevant challenges for both supervised and unsupervised learning tasks, in an increasing number of real world applications.

Tackling 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. For the industry, these tasks are in fact those that many already face today. Examples include the ability to prevent fraud, to anticipate catastrophes, and in general to enable more preemptive actions.

This workshop invites inter-disciplinary contributions to tackle the problems that many real-world domains face today. With the growing attention that this problem has collected, it is crucial to promote its development and to tackle its theoretical and application challenges.

The research topics of interest to LIDTA'2017 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
Understanding the nature of learning difficulties embedded in imbalanced data
Deep learning with imbalanced data
Handling imbalanced big data
One-class learning
Learning with non i.i.d. data
New approaches for data pre-processing (e.g. resampling strategies)
Post-processing approaches
Sampling approaches
Feature selection and feature transformation
Evaluation in imbalanced domains

***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

***Applications in imbalanced domains
Fraud detection (e.g. finance, credit and online banking)
Anomaly detection (e.g. industry, intrusion detection)
Health applications
Environmental applications (e.g. meteorology, biology)
Social media applications (e.g. popularity prediction, recommender systems)
Real world applications (e.g. oil spill detection)
Case studies



This workshop accepts two types of submissions: Full and Short Papers
For each of the accepted full papers, a presentation slot of 15 minutes is provided.
As for short papers, these will be introduced with short presentations, and a poster session will be organized.

* The maximum length for full papers is 12 pages and for the short papers the limit is 10 pages. Papers not respecting such limit will be rejected.
* All submissions must be written in English and follow the PMLR format. Instructions for authors and style files may be found in
* 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.
* Full 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.

To submit a paper, authors must use the on-line submission system hosted in EasyChair:



All accepted papers will be included in the workshop proceedings, published as a volume in Proceedings of Machine Learning Research (
Additionally, based on the success of the workshop, authors of selected papers will be invited to submit extended versions of their manuscripts to a premier journal concerning the topics of this workshop.



Roberto Alejo, Tecnológico de Estudios Superiores de Jocotitlán
Gustavo Batista, Universidade de São Paulo
Thomas Bäck, Leiden University
Colin Bellinger, University of Alberta
Seppe vanden Broucke, KU Leuven
Alberto Cano, Virginia Commonwealth University
Inês Dutra, Universidade do Porto
Christopher Drummond, University of Ottawa
Mikel Galar, Universidad Pública de Navarra
Wojtek Kowalczyk, Leiden University
Vasile Palade, University of Coventry
Rita Ribeiro, Universidade do Porto
Marina Sokolova, University of Ottawa
Isaac Triguero Velázquez, University of Nottingham
Michal Wozniak, Wroclaw University of Science and Technology
Ronaldo Prati, Universidade Federal do ABC



Luís Torgo | University of Porto, LIAAD - INESC TEC
Bartosz Krawczyk | Virginia Commonwealth University, Department of Computer Science
Paula Branco | University of Porto, LIAAD - INESC TEC
Nuno Moniz | University of Porto, LIAAD - INESC TEC

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