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Seminar abstract

Online Feature Selection with Streaming Features

Xindong Wu
Professor, IEEE/AAAS Fellow
University of Vermont, USA

Abstract: Online feature selection with streaming features refers to applications where the knowledge of the full feature space is unknown in advance and features flow in one by one over time. This is in contrast with traditional online learning methods that only deal with sequentially added data instances, with little attention being paid to streaming features. The critical challenges for online streaming feature selection include (1) the continuous growth of feature volumes over time, (2) a large feature space, possibly of unknown or infinite size, and (3) the unavailability of the entire feature set before learning starts. This talk introduces our recent research efforts on online streaming feature selection to select strongly relevant and non-redundant features on the fly.

Bio: Xindong Wu is a Yangtze River Scholar in the School of Computer Science and Information Engineering at the Hefei University of Technology (China), a Professor of Computer Science at the University of Vermont (USA), and a Fellow of the IEEE and AAAS. He received his Bachelor's and Master's degrees in Computer Science from the Hefei University of Technology, China, and his Ph.D. degree in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, knowledge-based systems, and Web information exploration. He received the 2012 IEEE Computer Society Technical Achievement Award "for pioneering contributions to data mining and applications", and the 2014 IEEE ICDM 10-Year Highest-Impact Paper Award.
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