Skip to main content

Advertisement

Springer Nature Link
Log in

Improved Alternative Queuing Method of Interval-Set Dissimilarity Measures and Possibility Degrees for Multi-expert Multi-criteria Decision-Making

  • Research
  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Multi-expert multi-criteria decision-making (MEMCDM) based on interval-set information is novel and valuable, and it already adopts an effective strategy of alternative queuing method (AQM), called AQM-IS. AQM-IS mainly relies on dissimilarity measures and possibility degrees of interval sets, and the two types of uncertainty measures have absolute-quantitative limitations on rough information extraction to imply improvement space. In this work, improved dissimilarity measures and possibility degrees of interval sets are constructed from a better perspective of relative quantization related to systematic structuring and statistical fusion, so improved AQM (called IAQM-IS) is established to advance MEMCDM by using the interval-set information transformation. As bases, relative dissimilarity measures are proposed to modify absolute dissimilarity measures for both interval-set pairs and families on closeness and deviation; thus, relevant internal relationships, mutual sizes, axiomatic properties, and illustrative examples are acquired. Aiming at interval-set information, improved AQM (i.e., IAQM-IS) is investigated for MEMCDM. Concretely, absolute dissimilarity measures are chosen to determine criterion weights based on judgement matrix and maximum deviation, and improved possibility degrees of interval sets are proposed by systematic likelihood characterizations and arithmetic mean combination; using the weight arithmetic mean of improved dissimilarity measures and possibility degrees, a more powerful index for sorting alternatives is generated to formulate IAQM-IS. For algorithmic evaluation, two assessment indices of decision rankings (called separability and goodness) are designed; accordingly, the two algorithms of MEMCDM — AQM-IS and IAQM-IS — are demonstrated and compared via both an applied examples of e-commerce platforms and six simulated experiments of public datasets, and thus the effectiveness and superiority of IAQM-IS are verified. In summary by the double-quantization technique, the improved dissimilarity measures and possibility degrees deepen uncertainty measures of interval-set information tables, and corresponding IAQM-IS has better decision performance than current AQM-IS in specific application scenarios of social cognition.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data Availability

No datasets were generated or analyzed during the current study.

References

  1. Pawlak Z. Rough sets: theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishers; 1991;9.

  2. Liu Z, Letchmunan S. Representing uncertainty and imprecision in machine learning: a survey on belief functions. J King Saud Univer-Comput Inf Sci. 2024;36:101904.

  3. Liu Z, Letchmunan S. Enhanced fuzzy clustering for incomplete instance with evidence combination. ACM Trans Knowl Discov Data. 2024;18:1–20.

    Article  Google Scholar 

  4. Zhang XY, Chen BW, Miao DQ. Systematic feature selection based on three-level improvements of fuzzy dominance three-way neighborhood rough sets. IEEE Trans Fuzzy Syst. 2024;32:5060–72.

    Article  Google Scholar 

  5. Zhang XY, Gou HY. Statistical-mean double-quantitative K-nearest neighbor classification learning based on neighborhood distance measurement. Knowl-Based Syst. 2022;250:109018.

  6. Long BH, Xu WH, Zhang XY, et al. The dynamic update method of attribute-induced three-way granular concept in formal contexts. Int J Approx Reason. 2020;126:228–48.

    Article  MathSciNet  Google Scholar 

  7. Mao H, Wang SY, Liu C, et al. Hypergraph-based attribute reduction of formal contexts in rough sets. Exp Syst Appl. 2023;234: 121062.

  8. Zhang XY, Yuan Z, Miao DQ. Outlier detection using three-way neighborhood characteristic regions and corresponding fusion measurement. IEEE Trans Knowl Data Eng. 2024;36:2082–95.

    Article  Google Scholar 

  9. Xu WH, Li WT. Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE T Cybern. 2016;46:366–79.

    Article  Google Scholar 

  10. Zhang XY, Yao YY. Tri-level attribute reduction in rough set theory. Exp Syst Appl. 2022;190: 116187.

  11. Lang GM, Cai MJ, Fujita H, Xiao QM. Related families based attribute reduction of dynamic covering decision information systems. Knowl Based Syst. 2018;162:161–73.

    Article  Google Scholar 

  12. Qian WB, Shu WH. Attribute reduction in incomplete ordered information systems with fuzzy decision. Appl Soft Comput. 2018;73:242–53.

    Article  Google Scholar 

  13. Li FC, Jin CX, Yang JN. Roughness measure based on description ability for attribute reduction in information system. Int J Mach Learn Cybern. 2019;10:925–34.

    Article  Google Scholar 

  14. Yao YY. Information granulation and rough set approximation. Int J Intell Syst. 2001;16:87–104.

    Article  Google Scholar 

  15. Yao YY. Interval-set algebra for qualitative knowledge representation[C]//Proceed the 5th Int J Conf Comput Inf. Washington, USA: IEEE, 1993;370–374.

  16. Yao YY, Q. Liu, A generalized decision logic in interval-set-valued information tables, In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining and Granular-Soft Computing, Springer, 1999;285–293.

  17. Li HX, Wang MH, Zhou XH, et al. An interval-set model for learning rules from incomplete information table. Int J Approx Reason. 2012;53:24–37.

    Article  MathSciNet  Google Scholar 

  18. Yao YY. Interval sets and three-way concept analysis in incomplete contexts. Int J Mach Learn & Cyber. 2017;8:3–20.

  19. Zhang YM, Jia XY, Tang ZM, et al. Uncertainty measures for interval-set information tables based on interval \(\delta \)-similarity relation. Inf Sci. 2019;501:272–92.

    Article  MathSciNet  Google Scholar 

  20. Zhang YM, Jia XY, Tang ZM. Information-theoretic measures of uncertainty for interval-set decision tables. Inf Sci. 2021;577:81–104.

    Article  MathSciNet  Google Scholar 

  21. Xie X, Zhang XY, Zhang SY. Rough set theory and attribute reduction in interval-set information system. J Intell Fuzzy Syst. 2022;42:4919–29.

    Article  Google Scholar 

  22. Liang DC, Fu YY, Xu ZS. Novel AQM analysis approach based on similarity and dissimilarity measures of interval-set for multi-expert multi-criterion decision making. Int J Approx Reason. 2022;142:266–89.

    Article  MathSciNet  Google Scholar 

  23. Fang R, Liao HC. A prospect theory-based evidential reasoning approach for multi-expert multi-criteria decision-making with uncertainty considering the psychological cognition of experts. Int J Fuzzy Syst. 2021;23:584–98.

    Article  Google Scholar 

  24. Wu XL, Liao HC. An approach to quality function deployment based on probabilistic linguistic term sets and ORESTE method for multi-expert multi-criteria decision making. Inf Fusion. 2018;43:13–26.

    Article  Google Scholar 

  25. Liao HC, Ren ZY, Fang R. A Deng-entropy-based evidential reasoning approach for multi-expert multi-criterion decision-making with uncertainty. Int J Comput Intell Syst. 2020;13:1281–94.

    Article  Google Scholar 

  26. Yan HB, Huynh VN, Nakamori Y, et al. On prioritized weighted aggregation in multi-criteria decision making. Exp Syst Appl. 2011;38:812–23.

    Article  Google Scholar 

  27. Khatari M, Zaidan AA, Zaidan BB, et al. Multi-criteria evaluation and benchmarking for active queue management methods: open issues, challenges and recommended pathway solutions. Int J Info Tech Dec Mak. 2019;18:1187–242.

    Article  Google Scholar 

  28. Zhang L, Zhan JM, Xu ZS, et al. Covering-based general multigranulation intuitionistic fuzzy rough sets and corresponding applications to multi-attribute group decision-making. Inf Sci. 2019;494:114–40.

    Article  Google Scholar 

  29. Anisseh M, Piri F, Shahraki MR, et al. Fuzzy extension of TOPSIS model for group decision making under multiple criteria. Artif Intell Rev. 2012;38:325–38.

    Article  Google Scholar 

  30. Liu P, Ali Z, Mahmood T, et al. Group decision-making using complex q-Rung orthopair fuzzy Bonferroni mean. IJCIS. 2020;13:822.

    Article  Google Scholar 

  31. Zhang XL, Liao HC, Xu B, et al. A probabilistic linguistic-based deviation method for multi-expert qualitative decision making with aspirations. Appl Soft Comput. 2020;93:106362.

  32. Wu WS, Xu ZS, Kou GK. Evaluation of group decision making based on group preferences under a multi-criteria environment. Technol Econ Develop Economy. 2020;26:1187–212.

    Article  Google Scholar 

  33. Lourenzutti R, Krohling RA. A generalized TOPSIS method for group decision making with heterogeneous information in a dynamic environment. Inf Sci. 2016;330:1–18.

    Article  Google Scholar 

  34. Tao R, Liu ZY, Cai R, et al. A dynamic group MCDM model with intuitionistic fuzzy set: perspective of alternative queuing method. Inf Sci. 2021;555:85–103.

    Article  MathSciNet  Google Scholar 

  35. Albahri OS, Zaidan AA, Salih MM, et al. Multidimensional benchmarking of the active queue management methods of network congestion control based on extension of fuzzy decision by opinion score method. Int J Intell Syst. 2021;36:796–831.

    Article  Google Scholar 

  36. Salih MM, Albahri OS, Zaidan AA, et al. Benchmarking of AQM methods of network congestion control based on extension of interval type-2 trapezoidal fuzzy decision by opinion score method. Telecommun Syst. 2021;77:493–522.

    Article  Google Scholar 

  37. Wen TC, Lai HH, Chang KH. A new flexible method for solving multi-expert multi-criterion decision-making problems. Appl Sci. 2020;10:4582.

    Article  Google Scholar 

  38. Hendiani S, Jiang LS, Sharifi E, et al. Multi-expert multi-criteria decision making based on the likelihoods of interval type-2 trapezoidal fuzzy preference relations. Int J Mach Learn & Cyber. 2020;11:2719–41.

  39. Tang GL, Yang YX, Gu XW, et al. A new integrated multi-attribute decision-making approach for mobile medical app evaluation under q-rung orthopair fuzzy environment. Exp Syst Appl. 2022;200:117034.

  40. Tang GL, Long JP, Gu XW, et al. Interval type-2 fuzzy programming method for risky multicriteria decision-making with heterogeneous relationship. Inf Sci. 2022;584:184–211.

    Article  Google Scholar 

  41. Tang GL, Gu XW, Chiclana F, et al. A multi-objective q-rung orthopair fuzzy programming approach to heterogeneous group decision making. Inf Sci. 2023;645:119343.

  42. Liu Z. An effective conflict management method based on belief similarity measure and entropy for multi-sensor data fusion. Artif Intell Rev. 2023;56:15495–522.

    Article  Google Scholar 

  43. Xie X, Zhang X. Systematic attribute reductions based on double granulation structures and three-view uncertainty measures in interval-set decision systems. Int J Approx Reason. 2024;109165.

  44. Liu Z. Fermatean fuzzy similarity measures based on Tanimoto and Sørensen coefficients with applications to pattern classification, medical diagnosis and clustering analysis. Eng Appl Art Intell. 2024;132:107878.

  45. Jiang JF, Zhang XY. Feature selection based on self-information combining double-quantitative class weights and three-order approximation accuracies in neighborhood rough sets. Inf Sci. 2024;657:119945.

  46. Li W, Deng C, Pedrycz W, Castillo O, Zhang C, Zhan T. Double-quantitative feature selection approach for multi-granularity ordered decision systems. IEEE Trans Art Intell. 2023;1–12.

  47. Huang B, Li H, Feng G, Guo C, Chen D. Double-quantitative rough sets, optimal scale selection and reduction in multi-scale dominance IF decision tables. Int J Approx Reason. 2021;130:170–91.

    Article  MathSciNet  Google Scholar 

  48. Lin G, Xie L, Li J, Chen J, Kou Y. Local double quantitative fuzzy rough sets over two universes. Appl Soft Comput. 2023;145:110556.

  49. Raharjo H, Endah D. Evaluating relationship of consistency ratio and number of alternatives on rank reversal in the AHP. Quality Eng. 2006;18:39–46.

    Article  Google Scholar 

  50. Sun H, Zhang XY. A probability-exponential method of converting Z-numbers and its systematic applications in multi-attribute decision making. J Intell Fuzzy Syst. 2024;46:6219–33.

    Article  Google Scholar 

  51. Li QC. Evaluation model and application of e-comerce platforms based on DANP. Haixiakexue. 2017;5:89–93.pagination

  52. D. DUA, C. Graff, UCI machine learning repository [OB/OL].[2017-05-10]. http://archive.ics.uci.edu/ml.

  53. Nahler G. Pearson correlation coefficient. In: Nahler G, editor. Dictionary of Pharmaceutical Medicine Vienna: Springer; 2009;132-32.

  54. Liao H, Xu Z, Zeng X-J. Novel correlation coefficients between hesitant fuzzy sets and their application in decision making. Knowl-Based Syst. 2015;82:115–27.

    Article  Google Scholar 

  55. Jin Y, Wu H, Sun D, Zeng S, Luo D, Peng B. A multi-attribute Pearson’s picture fuzzy correlation-based decision-making method. Math. 2019;7:999.

    Article  Google Scholar 

  56. Nasir IM, Khan MA, Yasmin M, Shah JH, Gabryel M, Scherer R, et al. Pearson correlation-based feature selection for document classification using balanced training. Sensors. 2020;20:6793.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank both the editors and reviewers for their valuable suggestions, which substantially improve this paper.

Funding

This work was supported by National Key Research and Development Program of China (2022YFB3103103), National Natural Science Foundation of China (62376198), Natural Science Foundation of Sichuan Province of China (2024NSFSC0486, 2024NSFSC0443), and Humanities and Social Sciences Project of the Ministry of Education of China (23YJA630114).

Author information

Authors and Affiliations

  1. School of Mathematical Sciences, Sichuan Normal University, Chengdu, 610066, China

    Xin Xie, Xianyong Zhang & Jiang Chen

  2. College of Artificial Intelligence, Chengdu University of Information Technology, Chengdu, 610225, China

    Zhiying Lv

  3. Research Center of Sichuan Normal University, National-Local Joint Engineering Laboratory of System Credibility Automatic Verification, Chengdu, 610066, China

    Xin Xie, Xianyong Zhang & Jiang Chen

  4. Visual Computing and Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu, 610066, China

    Xianyong Zhang

Authors
  1. Xin Xie
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xianyong Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zhiying Lv
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jiang Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Xin Xie: conceptualization, validation, methodology, visualization. Xianyong Zhang: conceptualization, methodology, formal analysis, supervision. Zhiying Lv: conceptualization, software, supervision. Jiang Chen: investigation, supervision.

Corresponding author

Correspondence to Xianyong Zhang.

Ethics declarations

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, X., Zhang, X., Lv, Z. et al. Improved Alternative Queuing Method of Interval-Set Dissimilarity Measures and Possibility Degrees for Multi-expert Multi-criteria Decision-Making. Cogn Comput 17, 72 (2025). https://doi.org/10.1007/s12559-025-10426-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12559-025-10426-0

Keywords