引用本文:蒋瑜.基于改进差别信息树的粗糙集属性约简算法[J].控制与决策,2019,34(6):1253-1258
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基于改进差别信息树的粗糙集属性约简算法
蒋瑜
(成都信息工程大学软件工程学院,成都610225)
摘要:
差别矩阵为属性约简提供了很好的思路,差别信息树能有效消除差别矩阵中的冗余元素,并实现对差别矩阵的压缩存储.然而,差别信息树既没有考虑“核”属性在消除差别矩阵中冗余元素的作用,也没有考虑属性序在压缩存储差别矩阵中非空元素的作用.对此,基于“核”属性和属性序关系,提出改进差别信息树,该树能进一步实现对差别矩阵中非空元素的压缩存储.最后,给出基于UCI数据库的仿真结果,并通过仿真结果验证该树的有效性.
关键词:  粗糙集  差别矩阵  属性约简  改进差别信息树  属性重要度
DOI:10.13195/j.kzyjc.2017.1523
分类号:TP18
基金项目:国家自然科学基金项目(61602064);四川省教育厅重点项目(17ZA0071);成都信息工程大学中青年学术带头人科研基金项目(J201609).
Attribute reduction with rough set based on improved discernibility information tree
JIANG Yu
(College of Software Engineering,Chengdu University of Information Technology,Chengdu 610225,China)
Abstract:
A discernibility matrix is a good idea for attribute reduction. Discernibility information tree can effectively eliminate redundancy elements in discernibility matrix, and it realizes the compactness storage of discernibility matrix. However, discernibility information tree neither considers the role of core attribute in eliminating redundancy elements nor considers the effect of attribute order in the compactness storage elements in the discernibility matrix. Therefore, an improved discernibility information tree is proposed, which can further compress the storage space of non-empty elements in the discernibility matrix. In order to verify the effectiveness of the improved discernibility information tree, some simulation experiments for UCI datasets are displayed.
Key words:  rough set  discernibility matrix  attribute reduction  improved discernibility information tree  attribute significance

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