粗糙集最优近似的动态更新方法

华东交通大学理学院,江西南昌 330013

人工智能; 粗糙集; 粒计算; 属性约简; 最优近似集; 增量更新; 相似度

A dynamic approach for updating the optimal approximation of rough set
LUO Laipeng and FAN Zizhu

School of Sciences, East China Jiaotong University, Nanchang 330013, Jiangxi Province, P.R.China

artificial intelligence; rough set; granular computing; attribute reduction; optimal approximation set; incremental updating; similarity degree

DOI: 10.3724/SP.J.1249.2021.03324

备注

目标概念在近似空间上的近似表示与计算是粗糙集理论模型研究的基础.对于动态属性约简及其相关问题往往需要更新粗糙集理论中目标概念的近似性,现有的更新方法都基于目标概念的上、下近似.提出一种基于最优近似的动态更新方法,针对新增单个对象引起决策系统中条件类与决策类的动态变化,讨论4种不同情形下决策类的最优近似动态更新机制,由此得到一种动态的最优近似集更新算法.在 UCI 数据集上的测试结果表明,4种不同情形下决策类的最优近似更新的时间复杂度存在差异,与非增量式更新方法相比,所提最优近似粗糙集的增量式更新方法可行且高效.
The approximate representation and calculation of target concept on approximate space are the foundation of rough set model study. For dynamic attribute reduction and related tasks, it is often necessary to update the approximation of the target concept of rough set theory, and most existing updating approaches are based on the lower and upper approximations of target concepts. This paper presents a dynamic approach for updating the optimal set when a single object is added into the decision table. Aiming at the dynamic change of condition class and decision class caused by the incremental object, the paper discusses the updating mechanisms of optimal approximation set in four different cases and then the dynamic updating algorithm of optimal approximation set is obtained. Finally, the experiments on different data sets from UCI show that the time complexity of optimal approximate updating of decision classes are different for four different cases and the incremental methods are feasible and efficient for decision systems of added one single object.
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