LIDTA 2017 @ ECML/PKDD 2017

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

Roberto Alejo, Tecnológico de Estudios Superiores de Jocotitlán
Thomas Bäck, Leiden University
Colin Bellinger, University of Alberta
Seppe vanden Broucke, KU Leuven
Alberto Cano, Virginia Commonwealth University
Vítor Cerqueira, Universidade do Porto
Inês Dutra, Universidade do Porto
Mikel Galar, Universidad Pública de Navarra
Wojtek Kowalczyk, Leiden University
Ronaldo Prati, Universidade Federal do ABC
Rita Ribeiro, Universidade do Porto
Marina Sokolova, University of Ottawa
Isaac Velásquez, University of Nottingham
Michal Wozniak, Wroclaw University of Science and Technology

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.

Submit your paper!