Skip to main content
Log in

An improved operator of combination with adapted conflict

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In the belief function theory, combination of reliable or unreliable information sources is concerned for a long time. Recently, Lefèvre and Elouedi proposed an operator called Combination With Adapted Conflict (CWAC) to synthesize all the knowledge of the initial belief functions. However, several problems are existed in the CWAC operator actually. The conflict obtained by using CWAC actually is not reasonable as an alarm in some situation and cannot truly reflect the opposition between the belief functions in the combination. In this paper, the existing problems of CWAC are exposed. And based on the spirit of original CWAC operator, an improved CWAC operator is proposed, which is more reasonable and effective. Some illustrative examples are given to show the effectiveness and strength of the proposed improved CWAC operator.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

References

  • Chen, C. W., & Fan, Y. (2012). Bioethanol supply chain system planning under supply and demand uncertainties. Transportation Research Part E: Logistics and Transportation Review, 48(1), 150–164.

    Article  Google Scholar 

  • Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. Annals of Mathematics and Statistics, 38(2), 325–339.

    Article  Google Scholar 

  • Deng, X., Hu, Y., Deng, Y., & Mahadevan, S. (2014a). Environmental impact assessment based on D numbers. Expert Systems with Applications, 41(2), 635–643.

    Article  Google Scholar 

  • Deng, X., Hu, Y., Deng, Y., & Mahadevan, S. (2014b). Supplier selection using AHP methodology extended by D numbers. Expert Systems with Applications, 41(1), 156–167.

    Article  Google Scholar 

  • Deng, X., Liu, Q., Hu, Y., & Deng, Y. (2013). TOPPER: Topology prediction of transmembrane protein based on evidential reasoning. Scientific World Journal 2013:Article ID 123,731, 8 pages. doi:10.1155/2013/123731.

  • Deng, X., Wang, Z., Liu, Q., Deng, Y., & Mahadevan, S. (2014c). A belief-based evolutionarily stable strategy. Journal of Theoretical Biology, 361, 81–86.

    Article  Google Scholar 

  • Deng, X., Zheng, X., Su, X., Chan, F. T., Hu, Y., Sadiq, R., et al. (2014d). An evidential game theory framework in multi-criteria decision making process. Applied Mathematics and Computation, 244, 783–793.

    Article  Google Scholar 

  • Deng, Y., Shi, W., Zhu, Z., & Liu, Q. (2004). Combining belief functions based on distance of evidence. Decision Support Systems, 38(3), 489–493.

    Article  Google Scholar 

  • Denoeux, T. (2008). Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artificial Intelligence, 172(2), 234–264.

    Article  Google Scholar 

  • Denoeux, T., & Masson, M. H. (2012). Evidential reasoning in large partially ordered sets. Annals of Operations Research, 195(1), 135–161.

    Article  Google Scholar 

  • Dezert, J., Han, D., Liu, Z., & Tacnet, J. M. (2012). Hierarchical proportional redistribution for bba approximation. In Belief functions: theory and applications (pp. 275–283). Berlin: Springer.

  • Dubois, D., & Prade, H. (1986). A set theoretic view of belief functions: Logical operations and approximations by fuzzy sets. International Journal Of General System, 12(3), 193–226.

    Article  Google Scholar 

  • Florea, M. C., Jousselme, A. L., Bossé, É., & Grenier, D. (2009). Robust combination rules for evidence theory. Information Fusion, 10(2), 183–197.

    Article  Google Scholar 

  • Fu, C., & Xu, D. L. (2014). Determining attribute weights to improve solution reliability and its application to selecting leading industries. Annals of Operations Research. doi:10.1007/s10479-014-1657-8.

  • Guo, H. W., Shi, W. K., & Deng, Y. (2006). Evaluating sensor reliability in classification problems based on evidence theory. IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics, 36(5), 970–981.

    Article  Google Scholar 

  • Han, D., Deng, Y., & Han, C. (2013). Sequential weighted combination for unreliable evidence based on evidence variance. Decision Support Systems, 56, 387–393.

    Article  Google Scholar 

  • Jousselme, A. L., Grenier, D., & Bossé, É. (2001). A new distance between two bodies of evidence. Information fusion, 2(2), 91–101.

    Article  Google Scholar 

  • Kang, B., Deng, Y., Sadiq, R., & Mahadevan, S. (2012). Evidential cognitive maps. Knowledge-Based Systems, 35, 77–86.

    Article  Google Scholar 

  • Kohlas, J. (1991). The reliability of reasoning with unreliable arguments. Annals of Operations Research, 32(1), 67–113.

    Article  Google Scholar 

  • Kuo, C. C. (2011). Optimal assignment of resources to strengthen the weakest link in an uncertain environment. Annals of Operations Research, 186(1), 159–173.

    Article  Google Scholar 

  • Lefèvre, E., & Elouedi, Z. (2013). How to preserve the conflict as an alarm in the combination of belief functions? Decision Support Systems, 56, 326–333.

    Article  Google Scholar 

  • Li, Z., & Kuo, C. C. (2013). Design of discrete dutch auctions with an uncertain number of bidders. Annals of Operations Research, 211(1), 255–272.

    Article  Google Scholar 

  • Lien, G., Hardaker, J. B., van Asseldonk, M. A., & Richardson, J. W. (2011). Risk programming analysis with imperfect information. Annals of Operations Research, 190(1), 311–323.

    Article  Google Scholar 

  • Liu, J., Yang, J. B., Ruan, D., Martinez, L., & Wang, J. (2008). Self-tuning of fuzzy belief rule bases for engineering system safety analysis. Annals of Operations Research, 163(1), 143–168.

    Article  Google Scholar 

  • Liu, Z., Dezert, J., Pan, Q., & Mercier, G. (2011). Combination of sources of evidence with different discounting factors based on a new dissimilarity measure. Decision Support Systems, 52(1), 133–141.

    Article  Google Scholar 

  • Ma, J., & Liu, W. (2011). A framework for managing uncertain inputs: An axiomization of rewarding. International Journal of Approximate Reasoning, 52(7), 917–934.

    Article  Google Scholar 

  • Ma, J., Liu, W., Dubois, D., & Prade, H. (2010). Revision rules in the theory of evidence. In 2010 22nd IEEE international conference on tools with artificial intelligence (ICTAI) (vol. 1, pp. 295–302).

  • Ma, J., Liu, W., & Miller, P. (2012). An evidential improvement for gender profiling. In Belief functions: theory and applications (pp. 29–36). Berlin: Springer.

  • Murphy, C. (2000). Combining belief functions when evidence conflicts. Decision Support Systems, 29(1), 1–9.

    Article  Google Scholar 

  • Özkan, E., & Kharoufeh, J. P. (2014). Incompleteness of results for the slow-server problem with an unreliable fast server. Annals of Operations Research. doi:10.1007/s10479-014-1615-5.

  • Pérez, J. C., Carrillo, M. H., & Montoya-Torres, J. R. (2014). Multi-criteria approaches for urban passenger transport systems: A literature review. Annals of Operations Research. doi:10.1007/s10479-014-1681-8.

  • Pishvaee, M., & Torabi, S. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets and Systems, 161(20), 2668–2683.

    Article  Google Scholar 

  • Shafer, G. (1976). A mathematical theory of evidence. Princeton: Princeton University Press.

    Google Scholar 

  • Smets, P., & Kennes, R. (1994). The transferable belief model. Artificial Intelligence, 66(2), 191–234.

    Article  Google Scholar 

  • Turgay, Z., Karaesmen, F., & Örmeci, E. L. (2014). A dynamic inventory rationing problem with uncertain demand and production rates. Annals of Operations Research. doi:10.1007/s10479-014-1573-y.

  • Van Den Berg, J., Abbeel, P., & Goldberg, K. (2011). LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information. The International Journal of Robotics Research, 30(7), 895–913.

    Article  Google Scholar 

  • Wu, D. D. (2009). Supplier selection in a fuzzy group setting: A method using grey related analysis and Dempster-Shafer theory. Expert Systems with Applications, 36(5), 8892–8899.

    Article  Google Scholar 

  • Xu, D. L. (2012). An introduction and survey of the evidential reasoning approach for multiple criteria decision analysis. Annals of Operations Research, 195(1), 163–187.

    Article  Google Scholar 

  • Xu, P., Deng, Y., Su, X., & Mahadevan, S. (2013). A new method to determine basic probability assignment from training data. Knowledge-Based Systems, 46, 69–80.

    Article  Google Scholar 

  • Yang, J. B., Liu, J., Wang, J., Sii, H. S., & Wang, H. W. (2006). A belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 36(2), 266–285.

    Article  Google Scholar 

  • Yang, J. B., & Singh, M. G. (1994). An evidential reasoning approach for multiple-attribute decision making with uncertainty. IEEE Transactions on Systems, Man and Cybernetics, 24(1), 1–18.

    Article  Google Scholar 

  • Yang, J. B., Wang, Y. M., Xu, D. L., Chin, K. S., & Chatton, L. (2012). Belief rule-based methodology for mapping consumer preferences and setting product targets. Expert Systems with Applications, 39(5), 4749–4759.

    Article  Google Scholar 

  • Zhang, Y., Deng, X., Wei, D., & Deng, Y. (2012). Assessment of E-commerce security using AHP and evidential reasoning. Expert Systems with Applications, 39(3), 3611–3623.

    Article  Google Scholar 

Download references

Acknowledgments

The work is partially supported by National Natural Science Foundation of China (Grant No. 61174022), Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20131102130002), R&D Program of China (2012BAH07B01), National High Technology Research and Development Program of China (863 Program) (Grant No. 2013AA013801), the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (Grant No. BUAA-VR-14KF-02), Fundamental Research Funds for the Central Universities (Grant No. XDJK2014D034).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Yong Deng or Felix T. S. Chan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, X., Deng, Y. & Chan, F.T.S. An improved operator of combination with adapted conflict. Ann Oper Res 223, 451–459 (2014). https://doi.org/10.1007/s10479-014-1729-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-014-1729-9

Keywords

Navigation