The workshop on Classifier Learning from Difficult Data is organized during the International Conference on Computational Science ICCS 2021 in Kraków, Poland.

About

Nowadays many practical decision task require to build models on the basis of data which included serious difficulties, as imbalanced class distributions, high number of classes, high-dimensional feature, small or extremely high number of learning examples, limited access to ground truth, data incompleteness, or data in motion, to enumerate only a few. Such characteristics may strongly deteriorate the final model performances. Therefore, the proposition of the new learning methods which can combat the mentioned above difficulties should be the focus of intense research. The main aim of this workshop is to discuss the problems of data difficulties, to identify new issues, and to shape future directions for research.

Topics of interest

  • Learning from imbalanced data
  • learning from data streams, including concept drift management
  • learning with limited ground truth access
  • learning from high dimensional data
  • learning with a high number of classes
  • learning from massive data, including instance and prototype selection
  • learning on the basis of limited data sets, including one-shot learning
  • learning from incomplete data
  • case studies and real-world applications

Key dates

Milestone Date
Paper submission 15 February 2021
Notification to authors 15 March 2021
Camera-ready papers 5 April 2021
Author registration 15 March – 5 April 2021
Non-author early registration 15 March – 23 April 2021
Non-author late registration from 24 April 2021
Conference sessions 16-18 June 2021

Keynote speaker

Paweł Ksieniewicz

Chosen Challenges of Imbalanced Data Stream Classification
Paweł Ksieniewicz is an assistant professor at Wroclaw University of Science and Technology, where he achieved M.Sc. degree in 2013 and Ph.D. degree in 2017. His research focuses on classification of imbalanced data streams, multidimensional data representation and image processing. Most of his papers concerns the hyperspectral imaging in context of data segmentation and visualization.

Program committee

  • Carlos Cambra, University of Burgos, Spain
  • Alberto Cano, Virginia Commonwealth University, USA
  • Sung-Bae Cho, Yonsei University, South Korea
  • Jose Alfredo F. Costa, Federal University (UFRN), Brazil
  • Richard J. Duro, Universidade da Coruña, Spain
  • Mohamed Medhat Gaber, Birmingham City University, UK
  • João Gama, University of Porto, Portugal
  • Salvador Garcia, University of Granada, Spain
  • Manuel Grana, University of the Basque Country, Spain
  • Francisco Herrera, Univeristy of Granada, Spain
  • Alvaro Herrero, University of Burgos, Spain
  • Michał Koziarski, AGH University, Poland
  • Bartosz Krawczyk, Virginia Commonwealth University, USA
  • Paweł Ksieniewicz, Wroclaw University of Science and Technology, Poland
  • Bernhard Pfahringer, University of Waikato, New Zealand
  • Piotr Porwik, Silesian University, Poland
  • Héctor Quintián, University of A Coruña, Spain
  • Jerzy Stefanowski, Poznań University of Technology, Poland
  • Arkadiusz Tomczyk, Łódź University of Technology, Poland
  • Michał Woźniak, Wroclaw University of Science and Technology, Poland

Organization commitee

Michał Woźniak, Wroclaw University of Science and Technology, Poland
Bartosz Krawczyk, Virginia Commonwealth University, USA
Paweł Ksieniewicz, Wroclaw University of Science and Technology, Poland
  • Weronika Borek, Wroclaw University of Science and Technology, Poland
  • Jędrzej Kozal, Wroclaw University of Science and Technology, Poland
  • Szymon Wojciechowski, Wroclaw University of Science and Technology, Poland
  • Paweł Zyblewski, Wroclaw University of Science and Technology, Poland