Call for Papers: Special Issue on Imbalanced Learning

Guest editors: 

  • Nuno Moniz, INESC TEC, Portugal
  • Paula Branco, University of Ottowa, Canada
  • Luís Torgo, Dalhousie University, Canada
  • Nathalie Japkowicz, American University, United States of America
  • Michal Wozniak, Wroclaw University of Science and Technology, Poland
  • Shuo Wang, University of Birmingham, United Kingdom

The problem of imbalanced domain learning has been discussed and studied for almost three decades. Although traditionally focused on classification tasks, the research community has started to address this problem in other contexts. These include multi-label and multi-class classification, regression, data streams, and many others. With an increasing number of real-world applications, we recognise the challenges posed by imbalanced domains as a broad and essential problem.

Tackling the 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 for complex tasks. These tasks are, in many cases, those that industry is already facing today. These are very diverse and include the ability to prevent fraud, anticipate catastrophes, and in general to enable preemptive action in highly impactful and rare events in an increasingly fast-paced world.

This special issue welcomes contributions detailing significant advances regarding the current state of the art in imbalanced learning. Specifically, we welcome contributions concerning theoretical foundations of imbalanced learning, providing insights into issues raised by imbalanced domains and evaluation challenges. Also, we aim to bring together contributions that describe novel approaches to solving imbalanced learning problems, including for example pre and post-processing strategies and feature selection approaches.

Topics of interest

  • Foundations of learning in imbalanced domains
    • Deep Learning
    • Imbalanced Big Data
    • One-Class Learning
    • New approaches to data pre-processing
    • Post-processing approaches
    • Feature Selection and Transformation
    • Evaluation Metrics and Methodologies
  • Knowledge discovery and machine learning in imbalanced domains
    • Classification, ordinal classification
    • Multi-label, multi-instance, sequence and association rules mining
    • Learning with imbalanced data streams
    • Imbalanced time series and spatio-temporal forecasting
    • Imbalanced regression
    • Graph classification with imbalanced data
    • Automated machine learning
    • Lifelong machine learning
  • Applications in imbalanced domains
    • Health applications (e.g. medical imaging)
    • Fraud detection (e.g. finance, credit and online banking)
    • Anomaly detection (e.g. industry, intrusion detection, privacy and security)
    • Environmental applications (e.g. meteorology, biology, oil spill detection)
    • Social media applications (e.g. popularity prediction, recommender systems)
    • Fake news detection and disinformation, deep fake classification

Contributions must contain original and unpublished work. All submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution and a discussion concerning its comparison to previous work will be key.

Submission Instructions

Submit manuscripts to: http://mach.edmgr.com. Select “SI: Imbalanced Domain Learning” as the article type. Early submissions are welcome. Papers must be prepared in accordance with the Journal guidelines: https://www.springer.com/journal/10994. All papers will be reviewed as soon as they are received, following standard reviewing procedures for the Journal.

Key Dates

  • Paper Submission Deadline: April 4, 2022
  • First Decision: July 4, 2022
  • Revision and resubmission deadline: October 15, 2022
  • Final Decision: January 17, 2023
  • Camera-ready: February 21, 2023
  • Publication/Wrap-up: March 2023