Approximate Feature Selection in Data-Driven Systems Modelling
讲座名称：
Approximate Feature Selection in Data-Driven Systems Modelling
讲座时间：
2017-04-14
讲座人：
Qiang Shen
形式：
校区：
兴庆校区
实践学分：
讲座内容：
应数学与统计学院的邀请，英国Aberystwyth大学Qiang Shen教授将于近期访问我院，并为师生作以下学术报告：
报告题目：Approximate Feature Selection in Data-Driven Systems Modelling
报告时间：4月14号（星期五）10:30
报告地点：理科楼407
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.
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