摘要
邻域粗糙集理论主要用于知识发现、属性选择、决策分析和数据挖掘等领域,能够根据数据的特点选择合适的离散化策略,在处理模糊和不确定性知识方面表现良好。但是,传统粗糙集属性约简算法存在难以确保获得约简、约简后的粗糙集属性识别准确率低等不足。对此,文中提出了一种基于属性重要度的属性约简算法。在充分考虑现有条件信息熵多方面不足的基础上,借鉴变精度邻域粗糙集理论对阈值参数进行重选,以新的条件信息熵作为度量基准,根据决策信息系统中的偏好属性推导出偏好决策规则集。对偏好决策规则集进行粗糙规则提取,并通过邻域粒化方法建立了变精度邻域粗糙集模型。该模型在处理大规模粗糙集属性数据时,计算时间较长,冗余属性过多。针对该问题,给出了一种属性重要度评价策略,在此基础上通过融合多叉树理论设计了变精度邻域粗糙集属性约简算法。实验结果表明,与传统方法相比,所提算法的属性识别准确率为92%,提高了10%左右,这充分验证了所设计的属性约简算法具有较强的有效性和较高的应用价值。
Neighborhood rough set theory is mainly used for knowledge discovery,attribute selection,decision analysis and data mining,and other fields.It can choose appropriate discretization strategy based on the characteristics of data and perform well in dealing with fuzzy and uncertain knowledge,but the traditional rough set attribute reduction algorithm is difficult to obtain reduction,and the attribute recognition accuracy of reduced rough set is low.Therefore,this paper put forward a kind of attribute reduction algorithm based on attribute importance.Considering the shortcomings of conditional information entropy in many aspects,the threshold parameters are re-selected by using the theory of varia-ble precision neighborhood rough set.Based on the new conditional information entropy as the measurement benchmark,the preference decision rule set is deduced according to the preference attributes in the decision information system.This paper extracted rough rules from preference decision rule set and established a variable precision neighborhood rough set model by using neighborhood granulation method.When dealing with large-scale rough set attribute data,this model takes a long time to calculate and has too many redundant attributes.Aiming at this problem,an evaluation strategy of attribute importance was given.Based on this,a variable precision neighborhood rough set attribute reduction algorithm was theoretically designed by fusing multi-tree.The experimental results show that compared with the traditional method,the accuracy of attribute recognition of the proposed method is 92%,which is improved by 10%.This fully verifies that the proposed attribute reduction algorithm has strong effectiveness and higher application value.
作者
郑文彬
李进金
何秋红
ZHENG Wen-bin;LI Jin-jin;HE Qiu-hong(School of Computer Science,Minnan Normal University,Zhangzhou,Fujian 363000,China;Lab of Granular Computing,Minnan Normal University,Zhangzhou,Fujian 363000,China;School of Mathematics and Statistics,Minnan Normal University,Zhangzhou,Fujian 363000,China)
出处
《计算机科学》
CSCD
北大核心
2019年第12期261-265,共5页
Computer Science
基金
国家自然科学基金项目(11871259)
国家自然科学青年基金项目(11701258)
福建省自然科学基金项目(2019J01749)资助
关键词
属性重要度
变精度邻域
粗糙集
属性约简
多叉树
Attribute importance
Variable-precision neighborhood
Rough set
Attribute reduction
Multi-way tree
