Dr. Bingyi Kang is now an Associate Professor of the College of Information Engineering at Northwest A&F University, Yangling, Shaanxi, China. He obtained his Ph.D. at Southwest University, Chongqing, China, in 2018. He was also a Visiting International Research Student (Joint Ph.D. student sponsored by CSC) at the University of British Columbia (Okanagan Campus) in 2016. Dr. Kang's research interest includes multi-source information fusion and intelligence information processing. He has published several papers in the journals such as IEEE Transactions on Fuzzy Systems, Knowledge-Based Systems，Applied Mathematics and Computation. He has been invited as a reviewer for the journals, e.g. IEEE Transactions on Fuzzy Systems, Information Sciences, International Journal of Approximate Reasoning, Robotics and Autonomous Systems, Computers & Industrial Engineering.

(1) Dempster-Shafer evidence theory (Evidence fusion, reasoning and learning)

(2) Z-number theory (Fuzzy decision making; Fuzzy probability inference)

(3) Uncertainty-aware machine learning models, algorithms and their applications in smart agriculture

Selected Publications:

[1] Bingyi Kang and Chunjiang Zhao, Deceptive evidence detection in information fusion of belief functions based on reinforcement learning[J], Information Fusion, vol. 103, pp. 102102, 2024.

[2] Huizi Cui, Yuhang Chang, Huaqing Zhang, XiangjunMi, and Bingyi Kang*, Determine the number of unknown targets in the open world from the perspective of bidirectional analysis using Gap statistic and Isolation forest[J], Information Sciences, vol. 623, pp. 832-856, 2023.

[3] Lingge Zhou, Huizi Cui, Xiangjun Mi Jianfeng Zhang, and Bingyi Kang*, A novel conflict management considering the optimal discounting weights using the BWM method in Dempster-Shafer evidence theory[J],  Information Sciences, vol.612, pp. 536-552, 2022.

[4] Ruonan Zhu, Qing Liu, Chongru Huang, and Bingyi Kang*, Z-ACM: An Approximate Calculation Method of Z-numbers for Large Data Sets Based on Kernel Density Estimation and Its Application in Decision-making[J], Information Sciences, vol.610, pp. 440-471, 2022.

[5] Ruolan Cheng, Jianfeng Zhang, Bingyi Kang*, Ranking of Z-numbers Based on the Developed Golden Rule Representative Value[J],  IEEE Transactions on Fuzzy Systems, vol.30, no. 12, pp. 5196-5210, 2022.

[6] Huizi Cui, Lingge Zhou, Yan Li, Bingyi Kang*, Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis[J],  Chaos, Solitons & Fractals, vol. 155, pp. 111736, 2022.

[7] Qing Liu, Huizi Cui, Ye Tian and Bingyi Kang*, On the Negation of discrete Z-numbers [J],  Information Sciences, vol. 537, pp. 18-29, 2020.

[8] Ye Tian, Lili Liu, Xiangjun Mi and Bingyi Kang*, ZSLF: A new soft likelihood function based on Z-numbers and its application in expert decision system [J], IEEE Transactions on Fuzzy Systems, vol. 29, no. 8, pp. 2283-2295, 2020.

[9] Bingyi Kang, Yong Deng, Kasun Hewage, and Rehan Sadiq. A method of measuring uncertainty for Z-number[J]. IEEE Transactions on Fuzzy Systems. vol. 27, no. 4, pp. 731-738, 2019.

[10] Bingyi Kang, Gyan Chhipi-Shrestha, Yong Deng, Kasun Hewage, and Rehan Sadiq. Stable strategies analysis based on the utility of Z-number in the evolutionary games[J].  Applied Mathematics and Computation. vol. 324, pp. 202-217, 2018.

[11] Bingyi Kang , Ya Li, Yong Deng, et al. Determination of Basic Probability Assignment Based on Interval Numbers and Its Application[J].  Acta Electronica Sinica . vol. 40, no. 6, pp. 1092-1096，2012.

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ResearchGate

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