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LIDTA 2017 : 1st International Workshop on Learning with Imbalanced Domains: Theory and Applications | |||||||||||||||
Link: http://lidta.dcc.fc.up.pt/ | |||||||||||||||
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Call For Papers | |||||||||||||||
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1st International Workshop on Learning with Imbalanced Domains: Theory and Applications (LIDTA 2017, co-located with ECML/PKDD 2017) 22nd September, 2017 Skopje, Macedonia Website: http://lidta.dcc.fc.up.pt/ ******************************************************************************************** The proceedings of this workshop will be published as a volume of the Proceedings of Machine Learning Research (PMLR) series. KEY DATES 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 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 ***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 *********************************************** SUBMISSION 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 http://ctan.org/tex-archive/macros/latex/contrib/jmlr/sample-papers * 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: https://easychair.org/conferences/?conf=lidta2017 *********************************************** PROCEEDINGS All accepted papers will be included in the workshop proceedings, published as a volume in Proceedings of Machine Learning Research (http://proceedings.mlr.press/). 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. *********************************************** PROGRAM COMMITTEE 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 *********************************************** ORGANIZERS 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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