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LIDTA 2018 : 2nd International Workshop on Learning with Imbalanced Domains - Theory and Applications (@ECML/PKDD 2018) | |||||||||||||||
Link: http://lidta.dcc.fc.up.pt | |||||||||||||||
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Call For Papers | |||||||||||||||
*Apologies for multi-posting*
************************************************************************************* LIDTA 2018, co-located with ECML/PKDD 2018 2nd International Workshop on Learning with Imbalanced Domains: Theory and Applications 10-14 September, Dublin, Ireland 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 (EXTENDED): Monday, July 9, 2018 Notification of Acceptance: Monday, July 23, 2018 Camera-ready Deadline: Monday, August 6, 2018 ECML/PKDD 2018: 10th-14th September, 2018 LIDTA 2018: TBA *********************************************** 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 recognised 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+tutorial is focused on providing a significant contribution to the problem of learning with imbalanced domains, and to increasing the interest and the contributions to solving some of its challenges. The tutorial component is designed to target researchers and professionals who have a recent interest on the subject, but also those who have previous knowledge and experience concerning this problem. The workshop component invites inter-disciplinary contributions to tackle the problems that many real-world domains face nowadays. With the growing attention that this problem has been collecting, it is important to promote its further development in order to tackle its theoretical and application challenges. *********************************************** The research topics of interest to LIDTA'2018 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 For each accepted paper, a presentation slot of 20 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. 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. * 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=lidta2018 *********************************************** 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 Gustavo Batista, Universidade de São Paulo Colin Bellinger, University of Alberta Seppe Vanden Broucke, Katholieke Universiteit Leuven Alberto Cano, Virginia Commonwealth University Inês Dutra, DCC - Faculty of Sciences, University of Porto Tom Fawcett, Apple Mikel Galar, Universidad Pública de Navarra Salvador García, Granada University Francisco Herrera, Granada University Jose Hernandez-Orallo, Universitat Politecnica de Valencia Ronaldo Prati, Universidade Federal do ABC Rita Ribeiro, DCC - Faculty of Sciences, University of Porto José Antonio Saez, University of Salamanca Shengli Victor Sheng, University of Central Arkansas Marina Sokolova, University of Ottawa Jerzy Stefanowski, Poznan University of Technology Isaac Triguero Velázquez, University of Nottingham Anibal R. Figueiras-Vidal, Universidad Carlos III de Madrid Shuo Wang, University of Birmingham Michal Wozniak, Wroclaw University of Science and Technology *********************************************** ORGANIZERS Luis Torgo | Dalhousie University Stan Matwin | Dalhousie University Nathalie Japkowicz | American University Bartosz Krawczyk | Virginia Commonwealth University Nuno Moniz | University of Porto, LIAAD - INESC TEC Paula Branco | University of Porto, LIAAD - INESC TEC |
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