LIDTA2021: 3rd International Workshop on Learning with Imbalanced Domains: Theory and Applications Online Bilbao, Spain, September 13-17, 2021 |
Conference website | http://lidta.dcc.fc.up.pt/ |
Submission link | https://easychair.org/conferences/?conf=lidta2021 |
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LIDTA 2021, co-located with ECML/PKDD 202021
3rd International Workshop on Learning with Imbalanced Domains: Theory and Applications
13-17 September, Online
Website: http://lidta.dcc.fc.up.pt/
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KEY DATES
- Submission Deadline: Sunday, July 4, 2021
- Notification of Acceptance: Sunday, July 18, 2021
- Camera-ready Deadline: Sunday, July 25, 2021
- ECML/PKDD 2021: 13-17 September, 2021
- LIDTA 2021: TBC
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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 the 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.
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The research topics of interest to the 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
- Regression
- Data streams and time series forecasting
- Clustering
- 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
- Energy-efficiency
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
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SUBMISSION
- For each accepted paper, a presentation slot of 15 minutes is provided.
- The maximum length for papers is 14 pages.
- All submissions must be written in English and follow the PMLR format. Instructions for authors and style files may be found here.
- 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.
- Based on such evaluation, submissions will be graded with 4 (excellent contribution), 3 (interesting idea, minor issues), 2 (could improve) or 1 (needs extensive revision)
- All submissions graded with 4 and 3 will be invited for oral presentation and will be included in the workshop proceedings.
- Submissions graded with 2 will be invited for poster presentation and their inclusion in the workshop proceedings will be decided by the organising committee, based on recommendations by the respective reviewers.
- At least one author of each accepted paper must attend the workshop and present the paper.
To submit a paper, authors must use the online submission system hosted in EasyChair
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PROCEEDINGS
All accepted papers will be included in the workshop proceedings, published as a volume in Proceedings of Machine Learning Research (PMLR). 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.
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PROGRAM COMMITTEE
(updating)
- Gustavo Batista, University of New South Wales
- Colin Bellinger, University of Alberta
- Seppe Vanden Broucke, Katholieke Universiteit Leuven
- Nitesh Chawla, University of Notre Dame
- Chris Drummond, NRC Institute for Information Technology
- Alberto Fernández, Granada University
- Mikel Galar, Universidad Pública de Navarra
- Salvador Garcia, University of Granada
- Raji Ghawi, Technical University of Munich
- Nikou Guennemann, Technical University of Munich
- Jose Hernandez-Orallo, Universitat Politecnica de Valencia
- Inaki Inza, University of the Basque Country
- Michał Koziarzki, AGH University of Science and Technology
- Bartosz Krawczyk, Virginia Commonwealth University
- Leandro Minku, University of Birmingham
- Ronaldo Prati, Universidade Federal do ABC - UFABC
- Rita Ribeiro, DCC - Faculty of Sciences, University of Porto
- Marina Sokolova, University of Ottawa
- Jerzy Stefanowski, Poznan University of Technology
- Herna Viktor, University of Ottawa
- Gary Weiss, Fordham University
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ORGANIZERS
- Nuno Moniz | INESC TEC / University of Porto, Portugal
- 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