[1] Dempster, A.P. (1967) Upper and Lower Probabilities Induced by a Multivalued Mapping. The Annals of Mathematical Statistics, 38, 325-339.

[2] Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press.

[3] Richter, S., Bieder, F., Wirges, S., Kinzig, C., & Stiller, C. (2022, July). Sensor data fusion in top-view grid maps using evidential reasoning with advanced conflict resolution. In 2022 25th International Conference on Information Fusion (FUSION) (pp. 1-7). IEEE.

[4] Sharma, M., & Maity, T. (2022, November). Fault-tolerant multi-sensor data fusion system for underground mine gas hazard prediction using Dempster Shafer Evidence Theory. In 2022 IEEE 6th Conference on Information and Communication Technology (CICT) (pp. 1-5). IEEE.

[5] Jin, J., Qu, T., Xu, R., Wang, X., & Cichocki, A. (2022). Motor imagery EEG classification based on Riemannian sparse optimization and dempster-shafer fusion of multi-time-frequency patterns. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 58-67.

[6] Zhao, J., Shi, Y., Liu, W., Zhou, T., Li, Z., & Li, X. (2023). A hybrid method fusing frequency recognition with attention detection to enhance an asynchronous brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 2391-2398.

[7] Han, D., Dezert, J., Tacnet, J. M., & Han, C. (2012, July). A fuzzy-cautious OWA approach with evidential reasoning. In 2012 15th International Conference on Information Fusion (pp. 278-285). IEEE.

[8] Xiao, F., Wen, J., & Pedrycz, W. (2022). Generalized divergence-based decision making method with an application to pattern classification. IEEE transactions on knowledge and data engineering, 35(7), 6941-6956.

[9] Han, D., Yang, Y., & Han, C. (2014). Advances in DS evidence theory and related discussions. Control and Decision, 29(1), 1-11.

[10] Selzer, F., & Gutfinger, D. (1989, January). LADAR and FLIR based sensor fusion for automatic target classification. In Sensor fusion: Spatial reasoning and scene interpretation (Vol. 1003, pp. 236-246). SPIE.

[11] Bi, Y., Bell, D., & Guan, J. (2004). Combining evidence from classifiers in text categorization. In Knowledge-Based Intelligent Information and Engineering Systems: 8th International Conference, KES 2004, Wellington, New Zealand, September 20-25, 2004, Proceedings, Part III 8 (pp. 521-528). Springer Berlin Heidelberg.

[12] Zhang, Z., Han, D., & Yang, Y. (2015, October). Image segmentation based on evidential Markov random field model. In 2015 International Conference on Control, Automation and Information Sciences (ICCAIS) (pp. 239-244). IEEE.

[13] Dezert, J., Liu, Z. G., & Mercier, G. (2011, July). Edge detection in color images based on DSmT. In 14th International Conference on Information Fusion (pp. 1-8). IEEE.

[14] Jiang, W., Duanmu, D., Fan, X., & Deng, Y. (2012, May). A new method to determine basic probability assignment under fuzzy environment. In 2012 International Conference on Systems and Informatics (ICSAI2012) (pp. 758-762). IEEE.

[15] Han, D., Han, C., & Deng, Y. (2013). Novel approaches for the transformation of fuzzy membership function into basic probability assignment based on uncertainty optimization. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 21(02), 289-322.

[16] Kang, B. Y., Li, Y. A., Deng, Y., Zhang, Y. J., & assignment based on interval numbers and its application. Dianzi Xuebao(Acta Electronica Sinica), 40(6), 1092-1096.

[17] Zhang, S., Han, D., & Yang, Y. (2020). Active learning based on belief functions. SCIENCE CHINA-INFORMATION SCIENCES, 63(11).

[18] Smets, P., & Kennes, R. (1994). The transferable belief model. Artificial intelligence, 66(2), 191-234.

[19] Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE transactions on pattern analysis and machine intelligence, 24(7), 881-892.

[20] Zhu, Q., Cai, Y., & Liu, L. (1999). A global learning algorithm for a RBF network. Neural Networks, 12(3), 527-540.

[21] Asuncion, A., & Newman, D. (2007, November). UCI machine learning repository. Retrieved from https://archive.ics.uci.edu/ml

[22] Kaggle. (n.d.). Kaggle: Your machine learning and data science community. Retrieved from https://www.kaggle.com

[23] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).