"Learning from imbalanced and difficult data"
Department of Computer Science, Virginia Commonwealth University, Richmond VA, USA
Bartosz Krawczyk is an assistant professor in the Department of Computer Science, Virginia Commonwealth University, Richmond VA, USA, where he heads the Machine Learning and Stream Mining Lab. He obtained his M.Sc. and Ph.D. degrees from Wroclaw University of Science and Technology, Poland, in 2012 and 2015 respectively. Dr. Krawczyk's current research interests include machine learning, data streams, ensemble learning, class imbalance, and explainable artificial intelligence. He has authored more than 60 journal papers and over 100 contributions to conferences. Dr. Krawczyk has coauthored the book Learning from Imbalanced Data Sets (Springer 2018). He was a recipient of numerous prestigious awards for his scientific achievements such as IEEE Richard Merwin Scholarship, IEEE Outstanding Leadership Award, and Amazon Machine Learning Award among others. He served as a Guest Editor for four journal special issues and as a Chair for fifteen special session and workshops. Dr. Krawczyk is a member of the Program Committee for conferences such as AAAI, IJCAI and IJCNN. He is the member of the editorial board for Applied Soft Computing (Elsevier).
Learning from imbalanced data is considered one of the vital challenges in contemporary machine learning. Despite more than three decades of research, the problem of handling skewed distributions is still as important as ever, with new challenges emerging on regular basis. This talk will give an overview of the imbalanced learning domain, focusing on contemporary challenging scenarios and recent developments. Special attention will be given to data-level difficulties and understanding minority classes, multi-class imbalanced problems, and data streams with dynamically evolving classes. The talk will discuss various resampling methods, low-dimensional embeddings, algorithm-level modifications, and ensemble learning approaches that were recently proposed to efficiently handle such challenging scenarios.