摘要
当决策系统增加新数据时,原约简集可能不再有效,这就需要对原约简集进行动态更新,目前已有的增量算法只研究了属性或样本的动态增加。本文从邻域粗糙集理论出发,详细分析系统在增加属性和样本数据后的变化规律,得到一种改进的增量式属性约简算法。该算法利用相对正域的概念对原约简集进行动态更新,可以处理属性和样本都增加的决策系统,有效地避免了二次约简过程。从理论上分析该算法的时间复杂度,实例表明该算法和传统算法的结论是一致的,实验证明该算法提高了计算效率。
The original reduction set may be invalid when new data are added to the decision system. Most of the existing incremental algorithms focus on increasing attributes or increasing samples. This paper analyzes the changing rules of the decision system after adding new attributes and samples. An improved incremental attribute reduction algorithm is presented with the framework of neighborhood rough sets, which can update the original reduction set dynamically by using the idea of relative positive region,and handle the two situations of incremental data mentioned above at the same time. The time complexity of the presented algorithm was analyzed and compared with the classical algorithm. The experiment results show that this conclusion accords with the attribute reduction obtained from traditional algorithm and the efficiency is improved.
出处
《广西师范大学学报（自然科学版）》
CAS
北大核心
2013年第3期45-50,共6页
Journal of Guangxi Normal University:Natural Science Edition
基金
国家自然科学基金资助项目(60975032)
山西省回国留学人员科研资助项目(2008-25)
山西省回国留学人员科研资助项目(2013-033)
山西省青年科技研究基金资助项目(2009021017-4)
关键词
邻域系统
增量式学习
相对正域
属性约简
neighborhood system
incremental learning
relative positive region
attribute reduction
作者简介
通信联系人：谢珺（1979～），女，山西五台人，太原理工大学副教授，博士。E—mail：xiejun@tyut．edu．cn
