关键词: 属性约简, 模糊邻域粗糙集, 依赖度, 知识粒度, 模糊邻域粒度

Abstract: Attribute reduction is the common data preprocessing method in such areas as machine learning. In the attribute reduction algorithm based on rough set theory, most of which based on single method to evaluate the importance of attribute. In order to achieve more accurate measurement from multiple perspectives for attribute, the concept of attribute dependency measurement is defined in the existed fuzzy neighborhood rough set model. Then, according to the conception of knowledge granularity in granular computing theory, the fuzzy neighborhood granularity measurement is proposed under the model of fuzzy neighborhood rough set. Because of the attribute dependency and knowledge granularity are representing a different respective of method of attribute evaluation, the two measurement methods are combined as the method of attribute importance evaluation for information system. Finally, a heuristic attribute reduction algorithm is given. The experimental results show that the proposed algorithm has better attribute reduction performance.

Key words: attribute reduction, fuzzy neighborhood rough set, dependency, knowledge granularity, fuzzy neighborhood granularity