摘要
实际应用中,信息系统的数据常常是动态变化的,当对象增加时,原始的属性约简集不一定有效。针对不完备决策系统对象增加的情况,提出基于条件熵的增量式属性约简算法。首先定义不完备决策系统中的条件熵,然后分析对象增加时条件熵的变化机制以及对约简集的影响,提出增量式属性约简算法,当对象增加时,该算法能够更高效地进行属性约简。最后,实验验证本文算法的有效性和高效性。
In practical applications, the data of information system is often dynamic. When the object is increased, the original attribute reduction set is not necessarily effective. An incremental attribute reduction algorithm based on the conditional entropy is proposed for the situation of incomplete decision system object being increased. Firstly, the conditional entropy in an incomplete decision system is defined, then the change mechanism of the conditional entropy and the influence on the reduction set are analyzed when the object is increased. An incremental attribute reduction algorithm is proposed, when the object is increased, the algorithm can be more efficient to reduce the attribute. Finally, experiments verify the effectiveness and efficiency of the proposed algorithm.
作者
王光琼
WANG Guang-qiong(School of Intelligent Manufacturing, Sichuan University of Arts and Science, Dazhou 635000, China;Dazhou Institute of Intelligent Manufacturing Industry Technology, Dazhou 635000, China)
出处
《计算机与现代化》
2019年第8期69-73,共5页
Computer and Modernization
基金
2019年中国物流学会、中国物流与采购联合会面上研究课题计划项目(2019CSLKT3-231)
四川省教育厅重点项目(18ZA0421,18ZA0419)
关键词
不完备决策系统
属性约简
条件熵
约简集
增量式
incomplete decision system
attribute reduction
conditional entropy
reduction set
incremental
