基于证据理论的多粒度决策背景最优粒度选取方法OA北大核心CSTPCD
Optimal Granularity Selection in Multi-granularity Decision Context Based on Evidence Theory
多粒度形式概念分析是数据挖掘与知识发现的重要工具.本文研究了覆盖多粒度下多粒度决策背景最优属性粒度组合选取方法.首先,基于覆盖属性粒化方法定义多粒度形式背景和多粒度决策形式背景,并且定义多粒度形式背景中的粗糙近似和信任结构.其次,基于粒协调性研究粒协调多粒度决策背景的最优属性粒度组合选取方法,并且证明最优属性粒度组合可以由证据理论中的信任函数刻画.最后,基于粗糙集理论和证据理论统一给出粒协调多粒度决策背景的最优属性粒度组合选取方法.
Multi-granularity formal concept analysis is an important tool for data mining and knowledge discovery.In this paper,we study the method of selecting the optimal granularity combination of attributes in multi-granularity formal decision context under covering multi-granularity.Firstly,based on the covering granularity method of attributes,multi-granularity formal context and multi-granularity formal decision context are defined,and rough approximation and belief structure in multi-granularity formal con-text are also defined.Secondly,based on granule consistency,the selection method of optimal granularity combination of attributes in granule consistent multi-granularity formal decision context is studied,and it is proved that the optimal granularity combination of attributes can be characterized by the belief function in evidence theory.Finally,based on rough set theory and evidence theory,the selection method of optimal granularity combination of attributes in granule consistent multi-granularity formal decision context is given.
王太滨;李德玉;翟岩慧
山西大学 计算机与信息技术学院,山西 太原 030006山西大学 计算机与信息技术学院,山西 太原 030006||山西大学 计算智能与中文信息处理教育部重点实验室,山西 太原 030006
计算机与自动化
形式概念分析多粒度最优粒度选取证据理论粗糙集
formal concept analysismulti-granularityoptimal granularity selectionevidence theoryrough set
《山西大学学报(自然科学版)》 2024 (004)
737-750 / 14
国家自然科学基金(61972238;62072294)
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