Optimal Granulation Selection for Multi-label Data Based on Local Generalized Multi-granulation Rough Set
LIANG Meishe1,2, MI Jusheng1 , HOU Chengjun1, JIN Chenxia1,3
1.College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024
2.Department of Scientific Development and School-Business Cooperation, Shijiazhuang University of Applied Technology, Shijiazhuang 050081
3.School of Economics and Management, Hebei University of Science and Technology, Shijiazhuang 050018
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.
梁美社, 米据生, 侯成军, 靳晨霞. 基于局部广义多粒度粗糙集的多标记最优粒度选择[J]. 模式识别与人工智能, 2019, 32(8): 718-725.
LIANG Meishe, MI Jusheng,HOU Chengjun, JIN Chenxia. Optimal Granulation Selection for Multi-label Data Based on Local Generalized Multi-granulation Rough Set. , 2019, 32(8): 718-725.
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2019, Vol. 32 
