LIDTA 2018 @ ECML/PKDD 2018

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+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

THE TUTORIAL (slides)

THE WORKSHOP

Roberto Alejo, Tecnológico Nacional de México/Instituto Tecnlógico de Toluca
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

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!