基于信息结构的区间值决策信息系统鲁棒不确定性度量

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.

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

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.