8th International Conference on Web Intelligence, Mining and Semantics
June 25 – 27 2018, Novi Sad, Serbia
Time-series classification is the common denominator in various recognition tasks, such as signature verification, person identification based on keystroke dynamics, detection of cardiovascular diseases and brain disorders (e.g. early stage of Alzheimer disease or dementia). This tutorial aims to give an overview of most prominent challenges (tasks), methods, evaluation protocols and biomedical applications related to time series classification. Besides the "conventional" time series classification task, early classification and semi-supervised classification will be considered. Both preprocessing techniques - Fourier transformation, SAX, etc. - and most prominent classifiers - such as similarity-based, feature-based, motif/shaplet-based classifiers and convolutional neural networks - will be covered. It will be pointed out that carefully designed evaluation protocols are required in order to assess the quality of the models fairly. This includes, depending on the application scenario, realistic assumptions about the availability of training data, careful (e.g. patient-based) train and test splits, etc. Selected applications will be explained, such as classification of functional magnetic resonance imaging (fMRI) data and person identification based on keystroke dynamics.
Krisztian Buza is currently a post-doc research assistant at the University of Bonn. He obtained his Diploma in Computer Science from the Budapest University of Technology and Economics, in 2007; and his Ph.D. from the University of Hildesheim in 2011. He is a co-author of more than 40 publications, including the "best paper" of the IEEE Conference on Computational Science and Engineering (2010) for his work on individualized error prediction for time series classification. His research focuses on time series classification and biomedical applications of machine learning and data mining.