基于局部广义多粒度粗糙集的多标记最优粒度选择

doi:10.16451/j.cnki.issn1003-6059.201908005

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Abstract

In multi-granulation rough set models, granulation selection is always related to positive region. Due to the excessive classification on the object set determined by all labels, few or none objects fall into the positive region, and a lot of information may be lost or even fail in positive reduction methods. To overcome this deficiency, an algorithm of optimal granulation selection for multi-label data based on local generalized multi-granulation rough set is proposed. Firstly, local generalized multi-granulation rough set model is introduced in multi-granulation and multi-label information system. Information level parameters are set, and the target set according to each label is approximated. The granularity quality of the multi-granulation and multi-label information system is defined, and then granular significance is obtained. Finally, a heuristic algorithm for optimal granularity selection is designed, and its effectiveness is verified.

Key words： Multi-label Data Multi-granulation Rough Set Optimal Granulation Selection Granular Significance

Received: 10 May 2019

ZTFLH:

TP 18

Fund:

Supported by National Natural Science Foundation of China(No.61573127), Natural Science Foundation of Hebei Province(No.A2018210120)

Corresponding Authors: LIANG Meishe (Corresponding author), Ph.D., lecturer. His research interests include rough set, granular computing and concept lattice.

About author:: MI Jusheng, Ph.D., professor. His research interests include granular computing and approximate reasoning.HOU Chengjun, master student. His research interests include rough set and granular computing.JIN Chenxia, Ph.D.candidate, associate professor. Her research interests include uncertain programming.

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	JIN Chenxia

Cite this article:

LIANG Meishe,MI Jusheng,HOU Chengjun等. Optimal Granulation Selection for Multi-label Data Based on Local Generalized Multi-granulation Rough Set[J]. , 2019, 32(8): 718-725.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201908005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I8/718

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