Keynote Speakers
Speaker: Dr. Michal Wozniak, Professor, Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland
Title of Talk: How to combat data imbalance in pattern classification tasks
Biography: Michal Wozniak is a professor of computer science at the Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Poland. His research focuses on machine learning, compound classification methods, classifier ensembles, data stream mining, and imbalanced data processing. Prof. Wozniak has been involved in research projects related to the topics mentioned above and has been a consultant for several commercial projects for well-known Polish companies and public administration. He has published over 300 papers and three books. He was awarded numerous prestigious awards for his scientific achievements as IBM Smarter Planet Faculty Innovation Award (twice) or IEEE Outstanding Leadership Award, and several best paper awards of the prestigious conferences. He serves as program committee chairs and the member for the numerous scientific events and prepared several special issues as the guest editor. He is the member of the editorial board of the high ranked journals as Information Fusion (Elsevier).
Talk description: Imbalanced data problem occurs whenever there is a significant disproportion among the number of instances in considered classes. It may pose a serious difficulty, often requiring the specially designed methods. In such cases, the most important consideration is often to properly detect minority examples, but at the same time, the performance on majority class cannot be neglected. Nevertheless, the imbalance ratio (even if it is high) usually does not pose a problem by itself. Only when combined with other data difficulty factors it negatively affects the minority class classification. Defining and understanding such factors is, therefore, a crucial task when designing new methods for dealing with imbalanced data.
Notwithstanding, the problem of imbalanced data analysis is the focus of intense research, but many open problems remain still unsolved in this domain. The talk will focus on the main approaches related to the imbalanced data classification and the open challenges in this domain, as multiclass imbalanced data classification, imbalanced stream learning.