Approximate Feature Selection in Data-Driven Systems Modelling
|讲座名称：||Approximate Feature Selection in Data-Driven Systems Modelling|
讲座题目：Approximate Feature Selection in Data-Driven Systems Modelling
报告摘要：Feature selection (FS) addresses the problem of selecting those system descriptors that are most predictive of a given outcome. 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. In particular, fuzzy-rough feature selection (FRFS) works with discrete and real-valued noisy data (or a mixture of both). 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.
个人简介：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 University’s Strategic Executive member and Director of the Institute of Mathematics, Physics and Computer Science, at Aberystwyth University. He is a Fellow of the Learned Society of Wales (aka., the Royal Society of Wales) and was a UK Research Excellence Framework (2008-2014) panel member for Computer Science and Informatics. His 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 350 peer-reviewed papers, including an award-winning IEEE Outstanding Transactions paper. He has served as the first supervisor of more than 50 PDRAs/PhDs, including one UK Distinguished Dissertation Award winner.
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