|2019-10-16 10:36 (浏览次数：)|
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 model and process (e.g., large-scale image analysis, text processing and Web content classification), where feature semantics play an important role.
This talk will focus on the development and application of approximate FS mechanisms based on rough and fuzzy-rough theories. Such techniques provide a means by which imprecisely described 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). As such, it is suitable for regression as well as for classification. The only additional information required is the fuzzy partition for each feature, which can be automatically derived from the data. FRFS has been shown to be a powerful technique for semantics-preserving 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 received a PhD in Knowledge-Based Systems and a DSc in Computational Intelligence. He holds the Established Chair of Computer Science and is a Pro Vice-Chancellor (i.e., Vice President in American English) at Aberystwyth University. He is a Fellow of the Learned Society of Wales and a UK Research Excellence Framework (2008-2014 and 2014-2021) panel member (for Computer Science and Informatics), one of the only two overseas Chinese scholars who have been twice appointed to such an important role across all assessment panels. He has been a long-serving Associate Editor or Editorial Board member of many leading international journals (e.g., IEEE Transactions on Cybernetics and IEEE Transactions on Fuzzy Systems), and has chaired and given keynotes at numerous international conferences.
Professor Shen’s current research interests include: computational intelligence, learning and reasoning under uncertainty, pattern recognition, data modelling and analysis, and their applications for intelligent decision support (e.g., space exploration, crime detection, consumer profiling, systems monitoring, and medical diagnosis). He has authored 2 research monographs and over 390 peer-reviewed papers, including an award-winning IEEE Outstanding Transactions paper. He has served as the first supervisor of more than 60 PDRAs/PhDs, including one UK Distinguished Dissertation Award winner.