Difficulties embedded within characteristics of real-life data pose various challenges to contemporary machine learning algorithms. The performance of learning algorithms may be strongly impaired by adverse data characteristics, such as data velocity, imbalanced distributions, high number of classes, high-dimensional feature spaces, small or extremely high number of learning examples, limited access to ground truth, data incompleteness, or concept drift (i.e., parameter change of the probabilistic characteristics describing data), to enumerate only a few.
The main aim of this section is to bring together researchers and scientists from basic computing disciplines (computer science and mathemathics) and researchers from various application areas who are pioneering data analysis methods in sciences, as well as in humanitarian fields, to discuss problems and solutions in the area of data difficulties, to identify new issues, and to shape future directions for research.

The list of possible topics includes, but is not limited to:

class imbalanced learning
learning from data streams
learning in the presence of concept drift
learning with limited ground truth access
learning from high dimensional data
learning on the basis of limited data sets, including one-shot learning
instance and prototype selection
data imputation methods
case studies and real-world applications affected by data difficulties

Session Chairs
Bartosz Krawczyk (bkrawczyk@vcu.edu),
Virginia Commonwealth University, USA
Michal Wozniak (michal.wozniak@pwr.edu.pl),
Wroclaw University of Science and Technology, Poland