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5月23日波兹南工业大学Roman Słowiński教授讲座预告
发布日期:2019-05-21 阅读:

讲座题目:Coping with Uncertainty in Knowledge Discovery using Dominance-based Rough Set Approach

主讲人:Roman Słowiński教授 波兹南工业大学

讲座时间:2019年5月23日(星期四)下午3:30-4:30

讲座地点:综合楼615

主讲人简介:Roman Słowiński,波兰科学院院士,欧洲科学院院士,IEEE(电气和电子工程师协会)院士,国际粗糙集学会院士,波兰信息科学院主席,波兹南工业大学教授,波兹南工业大学智能决策支持系统实验室的联合主席之一。Roman教授结合运筹学与智能计算对决策支持方法深入研究,提供医学、经济学和环境学领域决策支持技术。他以作者或共同作者的身份出版14本著作,400多篇论文,他的文章Hirsch引用指数高达78,被引次数超过10次的文章指数高达258,总引用超过24661次。

讲座摘要:Getting knowledge from massive data is nowadays a primary challenge for information processing. The goal of knowledge discovery from data describing decision situations is to help making better decisions. One of the difficulties in knowledge discovery is a vague character of data due to inconsistency. The Dominance-based Rough set Approach (DRSA) is a methodology for reasoning about vague data, which handles monotonic relationships between condition attributes and decision attributes, typical for data describing decision situations. The origin of the vagueness is inconsistency due to violation of the dominance principle which requires that (assuming a positive monotonic relationship) if object x has an evaluation at least as good as object y on all condition attributes, then it should not get evaluation worse than y on all decision attributes. We show that DRSA is a natural continuation of the Pawlak’s concept of rough set, which builds on the ideas coming from Leibniz, Frege, Boole, Łukasiewicz and Zadeh. We also show that the assumption admitted by DRSA about the ordinal character of evaluations on condition and decision attributes is not a limiting factor in knowledge discovery from data. In particular, it is an obvious assumption in decision problems, like multicriteria classification or ranking, multiobjective optimization, and decision under risk and uncertainty. Moreover, even when the ordering of data seems irrelevant, the presence or the absence of a property can be represented in ordinal terms, because if two properties are related, the presence, rather than the absence, of one property should make more (or less) probable the presence of the other property. This is even more apparent when the presence or the absence of a property is graded or fuzzy, because in this case, the more credible the presence of a property, the more (or less) probable the presence of the other property. This observation leads to a straightforward hybridization of DRSA with fuzzy sets. Since the presence of properties, possibly fuzzy, is the base of information granulation, DRSA can also be seen as a general framework for granular computing.



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