121
Views
11
CrossRef citations to date
0
Altmetric
Articles

Developed cosine similarity measure on belief function theory: An application in medical diagnosis

ORCID Icon & ORCID Icon
Pages 2858-2869 | Received 29 Dec 2018, Accepted 10 Jun 2020, Published online: 10 Sep 2020

References

  • Bhattacharyya, A. 1946. On a measure of divergence between two multinomial populations. Sankhyā: The Indian Journal of Statistics (1933-1960) 7 (4):401–406. http://www.jstor.org/stable/25047882.
  • Bouchard, M., A. Jousselme, and P. Doré. 2013. A proof for the positive definiteness of the Jaccard index matrix. International Journal of Approximate Reasoning 54 (5):615–26. doi:10.1016/j.ijar.2013.01.006.
  • Bui, D. T., B. Pradhan, O. Lofman, I. Revhaug, and O. B. Dick. 2012. Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): A comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40. doi:10.1016/j.catena.2012.04.001.
  • Chin, K., and C. Fu. 2015. Weighted cautious conjunctive rule for belief functions combination. Information Sciences 325:70–86. doi:10.1016/j.ins.2015.07.00.
  • Dempster, A. P. 1968. A generalization of Bayesian inference. Journal of the Royal Statistical Society: Series B (Methodological) 30 (2):205–32. doi:10.1111/j.2517-6161.1968.tb00722.x.
  • Deng, X., D. Han, J. Dezert, Y. Deng, and Y. Shyr. 2016. Evidence combination from an evolutionary game theory perspective. IEEE Transactions on Cybernetics 46 (9):2070–82. doi:10.1109/tcyb.2015.2462352.
  • Deng, Y. 2015. A threat assessment model under uncertain environment. Mathematical Problems in Engineering 2015:1–12. doi:10.1155/2015/87802.
  • Deng, Y., S. Mahadevan, and D. Zhou. 2015. Vulnerability assessment of physical protection systems: A bio-inspired approach. International Journal of Unconventional Computing 11 (34):27–43.
  • Denoeux, T., and M. Masson. 2004. EVCLUS: Evidential clustering of proximity data. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics: A Publication of the IEEE Systems, Man, and Cybernetics Society 34 (1):95–109. doi:10.1109/tsmcb.2002.806496.
  • Feng, T., J. Mi, and S. Zhang. 2014. Belief functions on general intuitionistic fuzzy information systems. Information Sciences 271:143–58. doi:10.1016/j.ins.2014.02.
  • Florea, M. C., and E. Bossé. 2009. Crisis management using Dempster Shafer theory: Using dissimilarity measures to characterize sources’ reliability C3I for Crisis. In Emergency and consequence management. Bucharest, Romania.
  • Florea, M. C., E. Bossé, and A. L. Jousselme. 2009. Metrics, distances and dissimilarity measures within Dempster-Shafer theory to characterize sources’ reliability. In Cognitive systems with interactive sensors (COGIS’09).
  • Frikha, A. 2014. On the use of a multi-criteria approach for reliability estimation in belief function theory. Information Fusion 18:20–32. doi:10.1016/j.inffus.2013.04.01.
  • Guo, H., W. Shi, and Y. Deng. 2006. Evaluating sensor reliability in classification problems based on evidence theory. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 36 (5):970–81. doi:10.1109/tsmcb.2006.87226.
  • Huijun, Z., L. Zuojun, C. Guoxing, and Z. Yan. 2016. Stumble mode identification of prosthesis based on the Dempster-Shafer evidential theory. 2016 Chinese Control and Decision Conference (CCDC), Yinchuan, May 28–30. doi:10.1109/ccdc.2016.7531457.
  • Jaccard, P. 1901. Etude comparative de la distribution florale dans une portion des Alpes et du Jura.
  • Jiang, W., C. Xie, B. Wei, and D. Zhou. 2016. A modified method for risk evaluation in failure modes and effects analysis of aircraft turbine rotor blades. Advances in Mechanical Engineering 8 (4):1–16. doi:10.1177/1687814016644579.
  • Jiang, W., Y. Yang, Y. Luo, and X. Qin. 2015. Determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers. International Journal of Computers Communications & Control 10 (3):333–47. doi:10.15837/ijccc.2015.3.1656.
  • Jiang, W., M. Zhuang, X. Qin, and Y. Tang. 2016. Conflicting evidence combination based on uncertainty measure and distance of evidence. SpringerPlus 5 (1):1217–28. doi:10.1186/s40064-016-2863-4.
  • Josang, A. 2002. The consensus operator for combining beliefs. Artificial Intelligence 141:157–70. doi:10.1016/S0004-3702(02)00259-X.
  • Jousselme, A., and P. Maupin. 2012. Distances in evidence theory: Comprehensive survey and generalizations. International Journal of Approximate Reasoning 53 (2):118–45. doi:10.1016/j.ijar.2011.07.006.
  • Lefèvre, E., and Z. Elouedi. 2013. How to preserve the conflict as an alarm in the combination of belief functions? Decision Support Systems 56:326–33. doi:10.1016/j.dss.2013.06.012.
  • Leung, Y., N. Ji, and J. Ma. 2013. An integrated information fusion approach based on the theory of evidence and group decision-making. Information Fusion 14 (4):410–22. doi:10.1016/j.inffus.2012.08.002.
  • Lin, Y., C. Wang, C. Ma, Z. Dou, and X. Ma. 2016. A new combination method for multisensor conflict information. The Journal of Supercomputing 72 (7):2874–90. doi:10.1007/s11227-016-1681-3.
  • Ma, M., and J. An. 2015. Combination of evidence with different weighting factors: A novel probabilistic-based dissimilarity measure approach. Journal of Sensors 2015:1–9. doi:10.1155/2015/50938.
  • Masri, H., and F. B. Abdelaziz. 2010. Belief linear programming. International Journal of Approximate Reasoning 51 (8):973–83. doi:10.1016/j.ijar.2010.07.003.
  • Peng, Y., H. Shen, Z. Hu, and Y. Ma. 2011. Clustering belief functions using extended agglomerative algorithm. International Journal of Image, Graphics and Signal Processing 3 (1):31–37. doi:10.5815/ijigsp.2011.01.05.
  • Salton, G. 1989. Automatic text processing. New York: Addison-Wesley.
  • Schubert, J. 2008. Clustering decomposed belief functions using generalized weights of conflict. International Journal of Approximate Reasoning 48 (2):466–80. doi:10.1016/j.ijar.2007.03.002.
  • Shafer, G. 1976. A mathematical theory of evidence. Princeton: Princeton University Press.
  • Silva, L. G., and A. T. Almeida-Filho. 2016. A multicriteria approach for analysis of conflicts in evidence theory. Information Sciences 346-347:275–85. doi:10.1016/j.ins.2016.01.08.
  • Song, Y., and Y. Deng. 2019. Divergence measure of belief function and its application in data fusion. IEEE Access 7 (1):107465–72. doi:10.1109/ACCESS.2019.2932390.
  • Song, Y., X. Wang, L. Lei, and A. Xue. 2014. Evidence combination based on credibility and separability. 2014 12th International Conference on Signal Processing (ICSP), China, Oct 19–23. doi:10.1109/icosp.2014.7015228.
  • Song, Y., X. Wang, and H. Zhang. 2015. A distance measure between intuitionistic fuzzy belief functions. Knowledge-Based Systems 86:288–98. doi:10.1016/j.knosys.2015.06.011.
  • Tang, H. 2015. A novel fuzzy soft set approach in decision making based on grey relational analysis and Dempster–Shafer theory of evidence. Applied Soft Computing 31:317–25. doi:10.1016/j.asoc.2015.03.01.
  • Wang, J., Y. Hu, F. Xiao, X. Deng, and Y. Deng. 2016. A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster–Shafer theory of evidence: An application in medical diagnosis. Artificial Intelligence in Medicine 69:1–11. doi:10.1016/j.artmed.2016.04.00.
  • Xiao, Z., X. Yang, Q. Niu, Y. Dong, K. Gong, S. Xia, and Y. Pang. 2012. A new evaluation method based on D–S generalized fuzzy soft sets and its application in medical diagnosis problem. Applied Mathematical Modelling 36 (10):4592–604. doi:10.1016/j.apm.2011.11.049.
  • Yang, J., and D. Xu. 2013. Evidential reasoning rule for evidence combination. Artificial Intelligence 205:1–29. doi:10.1016/j.artint.2013.09.003.
  • Yang, Y., and D. Han. 2016. A new distance-based total uncertainty measure in the theory of belief functions. Knowledge-Based Systems 94:114–23. doi:10.1016/j.knosys.2015.11.014.
  • Ye, J. 2011. Cosine similarity measures for intuitionistic fuzzy sets and their applications. Mathematical and Computer Modelling 53 (1-2):91–97. doi:10.1016/j.mcm.2010.07.022.
  • Ye, Q., X. Wu, and Z. Chen. 2009. An approach for evidence clustering using generalized distance. Journal of Electronics (China) 26 (1):18–23. doi:10.1007/s11767-008-0122-8.
  • Zhang, C., W. Zhu, and S. Yang. 2007. Banking operational risk management on DS evidence theory. 2007 International Conference on Wireless Communications, Networking and Mobile Computing, August, Shanghai, China. doi:10.1109/wicom.2007.1140.
  • Zhao, Y., R. Jia, and P. Shi. 2016. A novel combination method for conflicting evidence based on inconsistent measurements. Information Sciences 367-368:125–42. doi:10.1016/j.ins.2016.05.039.
  • Zhou, Z., F. Liu, L. Li, L. Jiao, Z. Zhou, J. Yang, and Z. Wang. 2015. A cooperative belief rule based decision support system for lymph node metastasis diagnosis in gastric cancer. Knowledge-Based Systems 85:62–70. doi:10.1016/j.knosys.2015.04.019.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.