An adaptive evidence combination method for decision analysis under uncertainty

References

• Abellan, J., & Bosse, E. (2020). Critique of recent uncertainty measures developed under the evidence theory and belief intervals. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(3), 1186–1192.
   Web of Science ®Google Scholar
• Achroufene, A., Amirat, Y., & Chibani, A. (2019). RSS-based indoor localization using belief function theory. IEEE Transactions on Automation Science and Engineering, 16(3), 1163–1180.
   Web of Science ®Google Scholar
• Aggarwal, M., & Tehrani, A. F. (2019). Modelling human decision behaviour with preference learning. INFORMS Journal on Computing, 31(2), 318–334.
   Web of Science ®Google Scholar
• Agreh, O. Y., & Ghaffari-Hadigheh, A. (2019). Application of Dempster-Shafer theory in combining the experts' opinions in DEA. Journal of the Operational Research Society, 70(6), 915–925.
   Web of Science ®Google Scholar
• Cinelli, M., Kadzinski, M., Gonzalez, M., & Słowinski, R. (2020). How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy. Omega, 96, 102261. https://doi.org/10.1016/j.omega.2020.102261
   Web of Science ®Google Scholar
• Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339. https://doi.org/10.1214/aoms/1177698950
   Google Scholar
• Dezert, J., & Smarandache, F. (2009). An introduction to DSmT. In F. Smarandache, & J. Dezert (Eds.), Advances and applications of DSmT for information fusion (collected works) (pp. 3–73).
   Google Scholar
• Dezert, J., Smarandache, F., & Khoshnevisan, M. (2004). Counter-examples to Dempster’s rule of combination. In F. Smarandache, & J. Dezert (Eds.), Advances and applications of DSmT for information fusion (pp. 105–121). American Research Press.
   Google Scholar
• Dong, G. G., & Kuang, G. Y. (2015). Target recognition via information aggregation through Dempster–Shafer’s evidence theory. IEEE Geoscience and Remote Sensing Letters, 12(6), 1247–1251.
   Web of Science ®Google Scholar
• Dubois, D., & Prade, H. (1986). A set-theoretic view on belief functions: Logical operations and approximations by fuzzy sets. International Journal of General Systems, 12, 193–226.
   Web of Science ®Google Scholar
• Fang, R., Liao, H. C., Yang, J. B., & Xu, D. L. (2021). Generalised probabilistic linguistic evidential reasoning approach for multi-criteria decision-making under uncertainty. Journal of the Operational Research Society, 72(1), 130–144.
   Web of Science ®Google Scholar
• Frikha, A., & Moalla, H. (2015). Analytic hierarchy process for multi-sensor data fusion based on belief function theory. European Journal of Operational Research, 241(1), 133–147.
   Web of Science ®Google Scholar
• Fu, C., Yang, J. B., & Yang, S. L. (2015). A group evidential reasoning approach based on expert reliability. European Journal of Operational Research, 246(3), 886–893.
   Web of Science ®Google Scholar
• Inagaki, T. (1991). Interdependence between safety-control policy and multiple-sensor schemes via Dempster-Shafer theory. IEEE Transactions on Reliability, 40(2), 182–188. https://doi.org/10.1109/24.87125
   Web of Science ®Google Scholar
• Kang, D., & Park, Y. (2014). Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Systems with Applications, 41(4), 1041–1050.
   Web of Science ®Google Scholar
• Khorshidi, H. A., & Aickelin, U. (2021). Multicriteria group decision making under uncertainty using interval data and cloud models. Journal of the Operational Research Society. https://doi.org/10.1080/01605682.2020.1796541
   Web of Science ®Google Scholar
• Li, D. M., Deng, L. B., & Cai, Z. M. (2021). Design of traffic object recognition system based on machine learning. Neural Computing and Applications. 33, 8143–8156. https://doi.org/10.1007/s00521-020-04912-9
   Web of Science ®Google Scholar
• Li, T. C., Fan, H. Q., García, J., & Corchado, J. M. (2019). Second-order statistics analysis and comparison between arithmetic and geometric average fusion: Application to multi-sensor target tracking. Information Fusion, 51, 233–243. https://doi.org/10.1016/j.inffus.2019.02.009
   Web of Science ®Google Scholar
• Li, F. L., & Lv, Z. H. (2017). Reliable vehicle type recognition based on information fusion in multiple sensor networks. Computer Networks, 117, 76–84.
   Web of Science ®Google Scholar
• Liu, B. S., Zhou, Q., Ding, R. X., Palomares, I., & Herrerace, F. (2019). Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination. European Journal of Operational Research, 275(2), 737–754.
   Web of Science ®Google Scholar
• Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J., McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (pp. 55–60).
   Google Scholar
• Medjkoune, S., Mouchere, H., Petitrenaud, S., & Viard-Gaudin, C. (2017). Combining speech and handwriting modalities for mathematical expression recognition. IEEE Transactions on Human-Machine Systems, 47(2), 259–272.
   Web of Science ®Google Scholar
• Murphy, C. K. (2000). Combining belief functions when evidence conflicts. Decision Support Systems, 29(1), 1–9. https://doi.org/10.1016/S0167-9236(99)00084-6
   Web of Science ®Google Scholar
• Qi, J., Yang, P., Newcombe, L., Peng, X. Y., Yang, Y., & Zhao, Z. (2019). An overview of data fusion techniques for internet of things enabled physical activity recognition and measure. Information Fusion, 55, 269–280.
   Web of Science ®Google Scholar
• Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001
   Web of Science ®Google Scholar
• Roy, P., Chowdhury, C., Kundu, M., Ghosh, D., & Bandyopadhyay, S. (2021). Novel weighted ensemble classifier for smartphone based indoor localization. Expert Systems with Applications, 164, 113758. https://doi.org/10.1016/j.eswa.2020.113758
   Web of Science ®Google Scholar
• Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15, 234–281. https://doi.org/10.1016/0022-2496(77)90033-5
   Web of Science ®Google Scholar
• Sentz, K., & Ferson, S. (2002). Combination of evidence in Dempster–Shafer theory. Technical report, SAND 2002-0835. Sandia National Laboratories.
   Google Scholar
• Shafer, G. (1976). A mathematical theory of evidence (Vol. 1). Princeton University Press.
   Google Scholar
• Smarandache, F., Dezert, J., & Tacnet, J. M. (2010). Fusion of sources of evidence with different importances and reliabilities. In BELIEF 2010: Workshop on the Theory of Belief Functions.
   Google Scholar
• Vijayabalaji, S., & Rameshb, A. (2019). Belief interval-valued soft set. Expert Systems with Applications, 119, 262–271.
   Web of Science ®Google Scholar
• Wu, X. L., & Liao, H. C. (2021). Learning judgment benchmarks of customers from online reviews. Or Spectrum. https://doi.org/10.1007/s00291-021-00639-8
   Web of Science ®Google Scholar
• Xiao, F. (2020). A new divergence measure for belief functions in D–S evidence theory for multisensor data fusion. Information Sciences, 514, 462–483. https://doi.org/10.1016/j.ins.2019.11.022
   Web of Science ®Google Scholar
• Yager, R. R. (1980). Competitiveness and compensation in decision making: A fuzzy set based interpretation. Computers & Operations Research, 7(4), 285–300.
   Web of Science ®Google Scholar
• Yager, R. R. (1987). Quasi-associative operations in the combination of evidence. Kybernetes, 16(1), 37–41. https://doi.org/10.1108/eb005755
   Web of Science ®Google Scholar
• Yang, J. B., & Xu, D. L. (2013). Evidential reasoning rule for evidence combination. Artificial Intelligence, 205, 1–29.
   Web of Science ®Google Scholar
• Zadeh, L. (1986). A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. AI Magazine, 7(2), 85–90.
   Google Scholar
• Zhang, L. (1994). Representation, independence, and combination of evidence in the Dempster-Shafer theory. In R. R. Yager, J. Kacprzyk, & M. Fedrizzi (Eds.), Advances in the Dempster-Shafer theory of evidence (pp. 51–69). John Wiley and Sons.
   Google Scholar