基于局部广义多粒度粗糙集的多标记最优粒度选择

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

在多粒度粗糙集模型中,粒度选择总是与正域有关.由于全体标记确定对象集上的分类过细,落入正域的对象很少或为空集,导致正域约简方法可能丢失大量信息甚至失效.为了克服这一缺陷,文中提出基于局部广义多粒度粗糙集的多标记最优粒度选择方法.首先,引入广义局部多粒度粗糙集的相关概念,通过设置信息水平参数,对单个标记的对象集合进行近似.然后,通过定义多粒度多标记信息系统的粒度质量,给出粒度重要性.最后,设计最优粒度选择的启发式算法,并通过实例验证文中方法的有效性.

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.