加拿大布鲁克大学荣誉教授Ivo Düntsch 学术报告 9月5日下午
发布时间: 2018-08-31 访问次数: 13

报告题目Rough sets: A tool for non–invasive knowledge discovery

  

时间: 201895日(周三)下午15:30


地点旗山校区数学与信息学院五楼507学术报告厅

  

主办: 数学与信息学院

  

主讲: 加拿大布鲁克大学Ivo Düntsch教授

  

参加:计算机系和软工系全体老师和研究生;其他感兴趣的老师和硕博研究生  

  

摘要: Rough set theory (RST) was introduced in the early 1980s by Z. Pawlak, and has become a well researched tool for knowledge discovery. The basic assumption of RST is that information is presented and perceived up to a certain granularity:

The information about a decision is usually vague because of uncertainty and imprecision coming from many sources . . .Vaguenessmay be caused by granularity of representation of the information. Granularity may introducean ambiguity to explanation or prescription based on vague information” (Pawlak andSłowi´nski,1993))

In contrast to other machine learning or statistical methods, the original rough set approach uses only the information presented by the data itself, and does not rely on outside distributional or other parameters. RST relies only on the principle of indifference and the nominal scale assumption. It has been applied in many fields, most recently in the investigation of complex adaptive systems, interactive granular computing, andbig data analysis.

In my talk I will present the basic concepts of RST as well as non–parametric methods for feature reduction,data filtering, significance testing and model selection. I will also give an overview of my academic development and my areas of expertise.