|Table of Contents|

Research on multi-label subspace based on variableprecision neighborhood rough sets(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2019年04期
Page:
414-422
Research Field:
Publishing date:

Info

Title:
Research on multi-label subspace based on variableprecision neighborhood rough sets
Author(s):
Zhang Jiahuan12Li Leijun12Li Meizheng34Mi Jusheng12Xie Bin34
1.College of Mathematics and Information Science,Hebei Normal University,Shijiazhuang 050024,China; 2.Hebei Key Laboratory of Computational Mathematics and Applications,Shijiazhuang 050024,China; 3.College of Information Technology,Hebei Normal University,Shijiazhuang 050024,China; 4.Hebei Key Laboratory of Network and Information Security,Shijiazhuang 050024,China
Keywords:
multi-label learning feature selection variable precision neighborhood rough set ensemblence
PACS:
O236
DOI:
10.14177/j.cnki.32-1397n.2019.43.04.006
Abstract:
Multi-label learning is a kind of hotspot problem on machine learning. In this paper,the variable precision neighborhood rough set is applied to feature selection in multi-label learning,and the different feature subspaces are analyzed systematically. Firstly,this paper proposes the variable precision neighborhood rough set model based on multi-label learning,and a feature selection method in multi-label learning is given. After that,different feature selection results,i.e. different feature subspaces,can be obtained based on different precisions and neighborhoods. In this paper,the influence of accuracy and neighborhood on feature subspace is analyzed in detail. The feature subspaces are integrated,and the corresponding performance is also analyzed.

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Last Update: 2019-09-30