Abstract
Due to various uncertain factors, sometimes it is difficult to obtain accurate data. In comparison, interval-valued data can better represent uncertain information. However, most of the existing theoretical researches on Extended Belief Rule-Based (EBRB) system are aimed at real-valued data and lack methods applied to interval-valued data. Based on the theory of interval evidence reasoning, this paper proposes a new method of constructing and reasoning for interval-valued EBRB systems. This model can not only be applied to interval-valued data but also real-valued data. And after analyzing the problems of the conventional EBRB similarity formula, we use the method of normalized Euclidean distance. In addition, because of the ignorance of attribute weights by conventional methods, this paper combines mutual information methods to determine attribute weights. Some case studies about interval-valued datasets and real-valued datasets are provided in the last. The experimental results have proven the feasibility and efficiency of the proposed method.
This research was supported by the National Natural Science Foundation of China (No. 61773123) and the Natural Science Foundation of Fujian Province, China (No. 2019J01647).
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Ye, JF., Fu, YG. (2021). Construction and Reasoning for Interval-Valued EBRB Systems. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13110. Springer, Cham. https://doi.org/10.1007/978-3-030-92238-2_22
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