[1]张海翔,李培培,胡学钢.基于自适应密度邻域关系的多标签在线流特征选择[J].计算机技术与发展,2024,34(01):23-29.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 004]
　ZHANG Hai-xiang,LI Pei-pei,HU Xue-gang.Multi-label Online Stream Feature Selection Based on Adaptive Density Neighborhood Relation[J].,2024,34(01):23-29.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 004]

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基于自适应密度邻域关系的多标签在线流特征选择()

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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

34

期数:

2024年01期

页码:

23-29

栏目:

大数据与云计算

出版日期:

2024-01-10

文章信息/Info

Title:

Multi-label Online Stream Feature Selection Based on Adaptive Density Neighborhood Relation

文章编号:

1673-629X(2024)01-0023-07

作者:

张海翔¹ ; 李培培² ; 胡学钢²

1. 蚌埠医学院附属合肥市第二人民医院 讯息处,安徽 合肥 230012;2. 合肥工业大学 大数据知识工程教育部重点实验室,安徽 合肥 230601

Author(s):

ZHANG Hai-xiang1 ; LI Pei-pei2 ; HU Xue-gang2

1. Information Division,The Second People’ s Hospital of Hefei Affiliated to Bengbu Medical College,Hefei 230012,China;

2. Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education, Hefei University of Technology,Hefei 230601,China

关键词:

多标签分类; 流特征; 邻域粗糙集; 自适应密度邻域; 在线流特征选择

Keywords:

multi - label classification;  streaming feature;  neighborhood rough set;  adaptive density neighborhood;  online streaming

分类号:

TP183

DOI:

10. 3969 / j. issn. 1673-629X. 2024. 01. 004

摘要:

流特征选择指从以流形式到来的特征数据中选出最优特征子集,现有方法大多在模型训练中需要事先学习领域信息并预设给定参数值。 实际应用中,由于不同的数据集数据结构和来源

不同,在模型学习过程中研究人员无法提前获取相关领域知识且针对不同类型数据集指定一个统一参数存在巨大挑战。 基于此,提出一种基于自适应密度邻域关系的多标签在线流特征

选择方法( multi-label online stream feature selection based on adaptive density neighborhood relation,ML-OFS-ADNR) ,基于邻域粗糙集理论,所提方法在特征依赖计算时无需任何先验领域信息。 此外,提出了一种新的自适应密度邻域关系,使用周围实例的密度信息,可以在流特征选择过程中自动选择适当数量的邻域,不需要事先指定任何参数。 通过模糊等价约束,ML-OFS-ADNR 可以选择高依赖低冗余度的特征。 实验表明在 10 种不同类型的数据集上,所提方法在特征数量相同的情况下优于传统特征选择方法和先进的在线流特征选择方法。

Abstract:

Stream feature selection selects the optimal feature subset from the feature data arriving in the form of stream. Most existingmethods require prior learning of domain information?

and presetting of given parameter values during model training. In real-world applications,due to the differences in data structure and source,researchers cannot obtain relevant domain information in advance during themodel learning process for different datasets, and it is a huge challenge for them to specify a unified parameter for different types ofdatasets. Motivated by this,we propose a multi - label online stream feature selection based on adaptive density neighborhood relation( ML- OFS - ADNR) . On the basis of the neighborhood rough set theory, the proposed method does not require any prior domaininformation in feature dependency calculation. Moreover,a new adaptive density neighborhood relationship is proposed,which can automatically select an appropriate number of neighborhoods in the streaming feature selection process using the density information ofsurrounding instances,and there is no need to specify any parameters in advance. By the fuzzy equal constraint,ML-OFS-ADNR canselect features with high dependency and low redundancy. Experimental studies on ten different types of data sets show that the proposedmethod is superior to traditional feature selection methods?

with the same numbers of features and state - of - the - art online streamingfeature selection algorithms in an online manner.

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