Willem Waegeman is Associate professor at the Department of Data Analysis and Mathematical Modelling of the Faculty of Bioscience Engineering of Ghent University. He is member of of the Knowledge-based Systems research unit KERMIT. His research activities are centred on machine learning and data science, including theoretical research and various applications in the life sciences. Specific areas of interest are multi-target prediction, deep learning, sequence models, and time series analysis.
Keywords: Multi-target Prediction, Deep Learning, Sequence Models, Time Series Analysis
K. Dembczynski, W. Waegeman , W. Cheng and E. Hüllermeier, On Label Dependence and Loss Minimization in Multi-label Classification, Machine Learning 88 (2012), 5-45.
W. Waegeman, T. Pahikkala, A. Airola, T. Salakoski, M. Stock and B. De Baets, A kernel framework for learning graded relations from data, IEEE Transactions on Fuzzy Systems 20 (2012), 1090-1111.
W. Waegeman, K. Dembczynski, W. Cheng and E. Hüllermeier, On the Bayes-optimality of F-measure maximizers, Journal of Machine Learning Research, 15 (1) (2014), 3333-3388.
K. Dembczynski, W. Waegeman and E. Hüllermeier, An analysis of chaining in multi-label classification, European Conference on Artificial Intelligence, Montpellier, France, Frontiers in Artificial Intelligence and Applications 242 (2012), 294-299.
W. Waegeman, K. Dembczynski and E. Hüllermeier, Multi-target prediction: a unifying view on problems and methods, Data Mining and Knowledge Discovery 33 (2019), 293-324.