主题
讲师
讲座地点
讲座时间
讲者简介
Ivo Düntsch教授，1981年获柏林自由大学自然科学博士学位，先后在德国奥斯纳布吕克大学、英国阿尔斯特大学、加拿大布鲁克大学工作，现任加拿大布鲁克大学计算机科学系荣誉教授和福建师范大学数学与信息学院讲座教授。主要从事人工智能、数据挖掘、机器学习、粗集、软件工程和关系数据库等领域的研究，成果丰硕，在相关研究领域的期刊和国际学术会议上发表学术论文140篇，先后担任欧盟委员会COST行动274“关系结构作为知识工具的理论和应用（TARSKI）”创始主席、爱尔兰数学学会学术会员、国际粗集学会高级会员和多个国际期刊的程序委员会委员以及国际学术会议负责人等，并多次受邀在顶级国际学术研讨会上作特邀报告。
摘要
Rough set theory (RST) was introduced in the early 1980s by Z. Pawlak (1982), 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 .Vagueness may be caused by granularity of representation ofthe information. Granularity may introduce an ambiguity to explanation or prescription based on vague information” (Pawlak and S?owinski, ?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, and big data analysis (Skowron et al., 2016).
In my talk I will present the basic concepts of RST as well as non–parametric methods for feature reduction, discretization, significance testing and model selection.
主办单位
计算机与控制工程学院
承办单位
福建省信息处理与智能控制重点实验室
协办单位
数字福建智能化生产物联网实验室
电子信息与控制工程研究中心
工业机器人应用福建省高校工程研究中心
福州市机器人技术应用联合实验室
闽江学院互联网创新研究中心
闽江学院汇川科技物联网联合研究院
闽江学院计算机与控制技术研究所
闽江学院2011协同创新培育点“物联网技术与智能系统”