基于信息结构的区间值决策信息系统鲁棒不确定性度量  

Robust Uncertainty Measurement for Interval-Valued Decision Information System via Information Structure

作　　者：吴溢洋 代建华 陈姣龙 WU Yiyang;DAI Jianhua;CHEN Jiaolong(Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing,Hunan Normal University,Changsha 410081;College of Information Science and Engineering,Hunan Normal University,Changsha 410081)

机构地区：[1]湖南师范大学智能计算与语言信息处理湖南省重点实验室,长沙410081 [2]湖南师范大学信息科学与工程学院,长沙410081

出　　处：《模式识别与人工智能》2020年第8期724-731,共8页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61976089,61473259);湖南省科技计划项目(No.2018TP1018,2018RS3065)资助。

摘　　要：现阶段基于单值的信息系统的不确定性度量研究较多,而少有关于区间值决策信息系统的不确定性和噪声标签对系统不确定性影响的研究.因此,文中提出基于信息结构的区间值决策信息系统鲁棒不确定性度量.利用KL散度定义区间值之间的相似度,构造区间值模糊相似关系,并提出区间值决策信息系统的信息结构.为了降低噪声决策对系统不确定性度量的影响,引入K近邻点计算样本关于决策的隶属度,提出2种基于信息结构的鲁棒不确定性度量方法.实验表明文中不确定性度量的有效性和合理性.Uncertainty measurement for single valued information system is widely studied.There are few researches on uncertainty measurement for interval-valued decision information system and the influence of the noise label on uncertainty measurement.Therefore,a robust uncertainty measurement for interval-valued decision information system via information structure is proposed.Firstly,the similarity degree between interval values is defined by KL divergence,and the fuzzy similarity relation of the interval values is constructed.Then,a information structure for interval-valued decision information system is proposed.In addition,K nearest neighbor points algorithm is introduced to calculate the membership degree of the samples about the decision,and two information structure based robust uncertainty measurement approaches are proposed to reduce the impact of noise labels on uncertainty measurement of systems.Finally,the validity and rationality of the proposed uncertainty measurement are verified through the experiments.

关 键 词：区间值数据 模糊粗糙集 不确定性度量 信息结构 KL散度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]