一种改进的不完全数据特征关系

Yin Xu-ri
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引用次数: 0

摘要

在经典粗糙集理论中,完全信息系统中使用的不可分辨关系在某些实际情况下可能过于严格。为了处理不完全数据,需要对不可分辨关系进行扩展。本文在讨论了不完全数据下特征关系的基本概念和研究现状的基础上,引入了一种改进的特征关系,该特征关系依赖于每个对象的完整定义属性的缺失值的数量;并给出了在此关系上定义的下近似和上近似。进一步给出了该修正特征关系的一些性质。实验表明,该关系在不完全信息下是有效的,可以合理地进行目标分类。这个电子文档是一个“实时”模板。论文的各个组成部分[标题,正文,标题等]已经在样式表中定义,如本文档中给出的部分所示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Modified Characteristic Relation for Incomplete Data
In the classical rough set theory, the use of the indiscernibility relation which is used in the complete information systems may be too rigid in some real situations. In order to process incomplete data, the indiscernibility relation needs to be extended. In this paper, after discussing the basic concepts and current research on the characteristic relation under incomplete data, a modified characteristic relation that is dependent on the number of missing values with respect to the number of the whole defined attributes for each object is introduced; the lower and upper approximation defined on this relation are proposed as well. Furthermore, we present some properties of this modified characteristic relation. The experiments show that this relation works effectively in incomplete information and generates object classification reasonably. This electronic document is a "live" template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
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