Optimal Granulation Selection for Multi-label Data Based on Local Generalized Multi-granulation Rough Set

被引：0
|
作者：
Liang M. [1 ,2 ]
Mi J. [1 ]
Hou C. [1 ]
Jin C. [1 ,3 ]
机构：
[1] College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang
[2] Department of Scientific Development and School-Business Cooperation, Shijiazhuang University of Applied Technology, Shijiazhuang
[3] School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Granular Significance; Multi-granulation Rough Set; Multi-label Data; Optimal Granulation Selection;
D O I：
10.16451/j.cnki.issn1003-6059.201908005
中图分类号：
学科分类号：
摘要：
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. © 2019, Science Press. All right reserved.
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页码：718 / 725
页数：7
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