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

Abstract

Abstract:

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

Key words: rough set, attribute reduction, neighborhood, mutual information entropy, conditional mutual information entropy

CLC Number:

TP18

Haibo LAN. Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data[J]. Big Data Research, 2022, 8(4): 133-144.

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References 20

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名称	样本数/个	属性数/个	分类数目/个
Cylinder	512	40	3
Credit	690	15	2
German	1 000	19	2
Segment	2 310	19	7
Sick	2 800	28	3
Abalone	4 177	8	29

对比项	原始数据集	对比算法1^[6]	对比算法2^[10]	对比算法3^[19]	本文算法
Cylinder	0.81±0.018	0.86±0.011	0.83±0.024	0.86±0.013	0.89±0.009
Credit	0.72±0.023	0.71±0.008	0.77±0.021	0.76±0.013	0.80±0.013
German	0.73±0.005	0.82±0.014	0.85±0.031	0.86±0.018	0.84±0.016
Segment	0.82±0.009	0.83±0.018	0.86±0.027	0.84±0.017	0.88±0.017
Sick	0.86±0.021	0.93±0.020	0.91±0.021	0.90±0.012	0.95±0.011
Abalone	0.82±0.040	0.89±0.014	0.86±0.043	0.83±0.025	0.89±0.016
平均	0.79±0.019	0.84±0.014	0.85±0.028	0.83±0.016	0.88±0.014

对比项	原始数据集	对比算法1^[6]	对比算法2^[10]	对比算法3^[19]	本文算法
Cylinder	0.80±0.021	0.83±0.015	0.82±0.022	0.85±0.011	0.88±0.012
Credit	0.74±0.017	0.77±0.014	0.79±0.026	0.83±0.015	0.81±0.018
German	0.74±0.009	0.86±0.018	0.83±0.027	0.84±0.012	0.87±0.014
Segment	0.80±0.012	0.82±0.015	0.84±0.015	0.83±0.018	0.86±0.016
Sick	0.83±0.014	0.90±0.021	0.88±0.017	0.89±0.014	0.93±0.013
Abalone	0.78±0.032	0.87±0.016	0.84±0.026	0.82±0.019	0.87±0.019
平均	0.78±0.018	0.84±0.016	0.83±0.022	0.85±0.015	0.87±0.015

Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data

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