混合型数据的邻域条件互信息熵属性约简算法

doi:10.11959/j.issn.2096-0271.2022066

摘要/Abstract

摘要：

属性约简是粗糙集理论的重要研究内容，其主要目的是消除信息系统中不相关的属性，降低数据维度并提高数据知识发现性能。然而，基于粗糙集的属性约简方法大多没有考虑到属性之间的依赖性，使得最终的属性约简结果存在一定的冗余属性，对此本文提出一种邻域条件互信息熵的属性约简算法。首先，在传统邻域熵的基础上，针对混合型的数据提出混合型邻域互信息熵模型和混合型邻域条件互信息熵模型；然后利用这两种熵模型进行混合型信息系统的属性依赖度评估和属性启发式搜索，并设计出一种属性约简算法；最后通过UCI数据集的实验分析，表明了本文算法具有较高的属性约简性能。

关键词: 粗糙集, 属性约简, 邻域, 互信息熵, 条件互信息熵

Abstract:

Attribute reduction is an important research content of rough set theory. Its main purpose is to eliminate irrelevant attributes in information system, reduce data dimension and improve data knowledge discovery performance. However, most of the attribute reduction methods based on rough set do not consider the dependence between attributes, which makes the final attribute reduction result have some redundant attributes. In this paper, an attribute reduction algorithm based on neighborhood conditional mutual information entropy is proposed. Firstly, based on the traditional neighborhood entropy, a hybrid neighborhood mutual information entropy model and a hybrid neighborhood conditional mutual information entropy model are proposed for hybrid data; Then the two entropy models are used to evaluate the attribute dependence and attribute heuristic search of hybrid information system, and an attribute reduction algorithm is designed; Finally, through the experimental analysis of UCI data sets, it is shows that the algorithm has higher attribute reduction performance.

Key words:

Rough set, Attribute reduction, Neighborhood, Mutual information entropy, Conditional mutual information entropy.

兰海波.

混合型数据的邻域条件互信息熵属性约简算法 [J]. 大数据, doi: 10.11959/j.issn.2096-0271.2022066.

LAN Haibo.

Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data [J]. Big Data Research, doi: 10.11959/j.issn.2096-0271.2022066.

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