计算机工程与应用2018，Vol.54Issue(9)：47-53,212,8.DOI:10.3778/j.issn.1002-8331.1709-0442
基于模糊邻域粗糙集的启发式属性约简算法
Heuristic attribute reduction algorithm based on fuzzy neighborhood rough set
摘要
Abstract
Attribute reduction is the common data preprocessing method in such areas as machine learning.In the attri-bute 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.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Key words
attribute reduction/fuzzy neighborhood rough set/dependency/knowledge granularity/fuzzy neighborhood granularity分类
信息技术与安全科学引用本文复制引用
任晓霞,薛凡..基于模糊邻域粗糙集的启发式属性约简算法[J].计算机工程与应用,2018,54(9):47-53,212,8.基金项目
国家自然科学基金面上项目(No.51377132) （No.51377132）
河北省科技厅计划项目(No.20160168). （No.20160168）
