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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
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