丢失数据恢复方法

E. Xu, Shaocheng Tong, Y. Wang, Shang Xu, Peng Li
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引用次数: 4

摘要

为了恢复信息系统中的缺失数据,本文提出了一种基于粗糙集的方法来减少冗余属性,离散连续属性,填充缺失数据。根据不可分辨关系,定义了可分辨向量,并利用可分辨向量相加规则进行属性约简。然后,利用超俱乐部数据的概念和信息表的熵,实现连续属性的离散化。最后,利用条件属性和决策属性的对应关系,定义了区间值的定义和区间值的加法规则,并对不完全数据进行了补全。实例和实验结果表明,该方法是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approach to Missing Data Recovery
In order to recover the missing data in an information system, the paper proposed a new approach based on rough set to reduce the redundant attributes, discretize the continuous attributes and fill in the missing data. According to indiscernible relationship, discernible vector were defined and used the discernible vector addition rule to reduce attributes. And then, depending on the concept of super-club data and entropy of the information table, discretization of the continuous attributes was implemented. Finally, by use of the corresponding relationship of condition attributes and decision attributes, the definition of interval value and interval value addition rule were defined and filled up the incomplete data. The illustration and experimental results indicate that the approach is effective and efficient.
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