"Online Oversampling Approaches for Class Imbalanced Data Stream Learning”
May 16th, at 16:00, Room FC6 0.29 (small auditorium), Department of Computer Science, FCUP
Abstract
The volume and incoming speed of data have increased tremendously over the past years. Data frequently arrive continuously over time in the form of streams, rather than forming a single static data set. Therefore, data stream learning, which is able to learn incoming data upon arrival, is an increasingly important approach to extract knowledge from data. Data stream learning is a challenging task, because the underlying probability distribution of the problem is typically not static, but suffers changes over time. Such challenge is exacerbated by the fact that the data distributions are often skewed. In classification problems, this means that the number of examples of a given class of interest is small compared to others, making adaptation to changes affecting such class difficult. In this talk, I will discuss online oversampling approaches that can be used to help tackling class imbalance in data stream learning.
Short biography
Dr. Leandro L. Minku is a Senior Lecturer (Associate Professor) at the School of Computer Science, University of Birmingham (UK). Prior to that, he was a Lecturer at the University of Leicester (UK), and a Research Fellow at the University of Birmingham (UK). He received the PhD degree in Computer Science from the University of Birmingham (UK) in 2010. Dr. Minku's main research interests include machine learning for non-stationary environments / data stream mining, class imbalance learning, machine learning for software engineering and search-based software engineering. Among other roles, Dr. Minku is Associate Editor-in-Chief for Neurocomputing, Senior Editor for IEEE Transactions on Neural Networks and Learning Systems, Associate Editor for Empirical Software Engineering journal and Associate Editor for Journal of Systems and Software. He was the general chair for the International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2019-2020), co-chair for the Artifacts Evaluation Track of the International Conference on Software Engineering (ICSE 2020), and technical chair for the 2023 IEEE Congress on Evolutionary Computation (CEC 2023).
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