中文摘要：

不平的集合理论是一个重要工具解决不明确的问题。当核心之一不平的集合理论发出，属性减小被证明了是为知识获得的一个有效方法。大多数启发式的属性减小算法通常保留使未改变并且忽略边界区域信息的一个目标的积极区域。那么，怎么在一个多使成粒状空格从一个目标集合的边界区域获得知识是一个有趣的问题。在这份报纸，一个新概念，不平的集合的一个近似集合的模糊第一被提出。然后，变化在改变颗粒度模糊裁定空格被分析。最后，为属性减小的一个新算法基于设置的 0.5 近似的模糊被介绍。几试验性的结果证明由建议方法的属性减小与各种各样的分类算法相比有相对更好的分类特征。

英文摘要：

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.