Robust Uncertainty Measurement for Interval-Valued Decision Information System via Information Structure
WU Yiyang1,2, DAI Jianhua1,2, CHEN Jiaolong1,2
1. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081 2. College of Information Science and Engineering, Hunan Normal University, Changsha 410081
Abstract 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.
Fund:Supported by National Natural Science Foundation of China(No.61976089, 61473259), Science and Technology Project of Hunan Province(No.2018TP1018, 2018RS3065)
Corresponding Authors:
DAI Jianhua, Ph.D., professor. His research interests include artificial intelligence, fuzzy sets, rough sets, intelligent information processing and machine learning.
About author:: WU Yiyang, master student. His research interests include rough sets, fuzzy sets and data mining.
WU Yiyang,DAI Jianhua,CHEN Jiaolong. Robust Uncertainty Measurement for Interval-Valued Decision Information System via Information Structure[J]. , 2020, 33(8): 724-731.
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2020, Vol. 33 
