Attribute reduction based on fuzziness of approximation set in multi-granulation spaces

Rough set theory is an important tool to solve uncertain problems. Attribute reduction, as one of the core issues of rough set theory, has been proven to be an effective method for knowledge acquisition. Most of heuristic attribute reduction algorithms usually keep the positive region of a target set unchanged and ignore boundary region information. So, how to acquire knowledge from the boundary region of a target set in a multi-granulation space is an interesting issue. In this paper, a new concept, fuzziness of an approximation set of rough set is put forward firstly. Then the change rules of fuzziness in changing granularity spaces are analyzed. Finally, a new algorithm for attribute reduction based on the fuzziness of 0.5-approximation set is presented. Several experimental results show that the attribute reduction by the proposed method has relative better classification characteristics compared with various classification algorithms.

议程设置理论概览:过去,现在与未来

本文对议程设置理论的五个研究领域,即议程设置的基本效果、属性议程设置、议程设置效果的心理学、媒介议程的来源和议程设置效果的后果,自该理论创立以来所取得的主要发现作了简要回顾,并基于此讨论了议程设置理论的几个新研究领域,包括对议程设置理论基本概念与观点的在更多新领域的应用,比如企业声誉、教育、宗教组织等,这些新领域已远不是议程设置理论最初生成的公共事务领域了。作为该理论的创立者之一,作者在文章最后提出了对于议程设置研究的几点个人观察。