报告题目：Feature Selection: Rough and Fuzzy-Rough Approaches
报 告 人：Professor Qiang Shen
主 持 人：李映 教授
Feature selection (FS) addresses the problem of selecting those system descriptors that are most predictive of a given outcome. Unlike other dimensionality reduction methods, with FS the original meaning of the features is preserved. This has found application in tasks that involve datasets containing very large numbers of features that might otherwise be impractical to process (e.g., large-scale image analysis, text processing and Web content classification).
FS mechanisms developed on the basis of rough and fuzzy-rough theories provide a means by which data can be effectively reduced without the need for user-supplied information. In particular, fuzzy-rough feature selection (FRFS) works with discrete and real-valued noisy data (or a mixture of both), and can be applied to continuous or nominal decision attributes. As such, it is suitable for regression as well as classification. The only additional information required is in the form of fuzzy partitions for each feature that can be automatically derived from the data. FRFS has been shown to be a powerful technique for data dimensionality reduction. In introducing the general background of FS, this talk will first cover the rough-set-based approach, before focusing on FRFS and its application to real-world problems. The talk will conclude with an outline of opportunities for further development.
Professor Qiang Shen holds the Established Chair of Computer Science and is Director of the Institute of Mathematics, Physics and Computer Science (and currently also Head of the Department of Computer Science) at Aberystwyth University. He is a Fellow of the Learned Society of Walesand a member of UK REF 2014 Subpanel 11: Computer Science & Informatics. Qiang was a London 2012 Olympic Torch Relay torchbearer, nominated to carry the Olympic torch in memory of Alan Turing.
Professor Shen’s current research interests include: computational intelligence, reasoning under uncertainty, pattern recognition, data mining, and their real-world applications for intelligent decision support (e.g., crime detection, consumer profiling, systems monitoring, and medical diagnosis). He has authored 2 research monographs and over 300 peer-reviewed papers, including an award-winning IEEE Outstanding Transactions paper. Qiang has served as the first supervisor of over 40 PDRAs/PhDs, including one UK Distinguished Dissertation Award winner.