LIDTA' 2017 - 1st International Workshop on Learning with Imbalanced Domains: Theory and Applications (co-located with ECML/PKDD 2017)

Website http://lidta.dcc.fc.up.pt/ | Edit Freely

Category machine learning; data mining; imbalanced domains; predictive modelling; applications; computer science; artificial intelligence; big data

Deadline: July 03, 2017 | Date: September 22, 2017

Venue/Country: Skopje, Macedonia

Updated: 2017-06-10 04:04:30 (GMT+9)

Call For Papers - CFP

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 3, 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