Skip to main content

A Web Based Assessment Tool via the Evidential Reasoning Approach

  • Chapter
Computational Intelligence in Complex Decision Systems

Part of the book series: Atlantis Computational Intelligence Systems ((ATLANTISCIS,volume 2))

  • 633 Accesses

Abstract

This chapter describes the interface design and the applications of a web based assessment tool on the basis of the Evidential Reasoning (ER) approach. The tool has been validated and used by a number of business users and examples of the application areas include business innovation assessment, supplier evaluation, and performance assessment. The ER approach is developed to handle multi-criteria decision analysis (MCDA) and assessment problems possibly with hybrid types of uncertainties. It uses belief decision matrices for problem modelling and data collection. Combined with thoughtfully designed interfaces, the tool offers a flexible and friendly environment for web users to express their assessment judgments consistently, objectively and accurately. By applying the ER algorithm for information aggregation, assessment outcomes generated from the tool can include average scores and ranking, sensitivity of the outcomes to uncertainties in different parameters, distributed assessments revealing variations in performances, and user specific reports highlighting key areas for attention. The interfaces of the tool allow users to enter data using a built-in assessment criteria hierarchy and to display aggregated outcomes in both text and graphic formats.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. V. Belton and T. J. Stewart (2002), Multiple Criteria Decision Analysis — An Integrated Approach, Kluwer Academic Publishers.

    Google Scholar 

  2. M. Beynon, D. Cosker and D. Marshall (2001), “An expert system for multi-criteria decision making using Dempster Shafer theory”, Expert Systems with Applications, 20, 357–367.

    Article  Google Scholar 

  3. M. Beynon, B. Curry and P. Morgan (2000), “The Dempster-Shafer theory of evidence: an alternative approach to multicriteria decision modelling”, Omega, 28, 37–50.

    Article  Google Scholar 

  4. B. G. Buchanan and E. H. Shortliffe (1984), Rule-Based Expert Systems, Reading, MA: Addison-Wesley.

    Google Scholar 

  5. D. Cai,M. F.McTear and S. I.McClean (2000), “Knowledge discovery in distributed databases using evidence theory”, International Journal of Intelligent Systems, 15, 745–761.

    Article  MATH  Google Scholar 

  6. K. S. Chin, J. B. Yang, J. Lam and M. Guo (2007), “An evidential reasoning-interval based method for new product design assessment”, IEEE transactions on Engineering Management, in press.

    Google Scholar 

  7. T. A. Conti (2007), “A history and review of the European Quality Award Model”, The TQM Magazine, 19 (2), 112–128.

    Article  Google Scholar 

  8. T. Denoeux (1997), “Analysis of evidence-theoretic decision rules for pattern classification”, Pattern Recognition, 30 (7), 1095–1107.

    Article  Google Scholar 

  9. S. French (1986), Decision theory - An introduction to the mathematics of rationality, Ellis Horwood, Chichester.

    MATH  Google Scholar 

  10. R. P. H¨am¨al¨ainen (2003), “Decisionarium-aiding decisions, negotiating and collecting opinions on the web”, Journal of Multi-Criteria Decision Analysis, 12 (2-3), 101–110.

    Article  Google Scholar 

  11. C. L. Hwang and K. Yoon (1981), Multiple Attribute Decision Making Methods and Applications, Springer-Verlag.

    MATH  Google Scholar 

  12. Q. Ji and M. M. Marefat (2003), “A Dempster–Shafer approach for recognizing machine features from CAD models”, Pattern Recognition, 36 (6), 1355–1368.

    Article  MATH  Google Scholar 

  13. R. L. Keeney and H. Raiffa (1976), Decisions with Multiple Objectives, Cambridge University Press.

    Google Scholar 

  14. A. A. Salo and R. P. H¨am¨al¨ainen (1995), “Preference programming through approximate ratio comparisons”, European Journal of Operational Research, 82, 458–475.

    Article  MATH  Google Scholar 

  15. A. Saltelli, S. Tarantola and K. Chan (1999), ”A quantitative, model independent method for global sensitivity analysis of model output”, Technometrics, 41 (1), 39–56

    Article  Google Scholar 

  16. P. Sen and J. B. Yang (1995), “Multiple criteria decision making in design selection and synthesis”, Journal of Engineering Design, 6 (3), 207–230.

    Article  Google Scholar 

  17. G. A. Shafer (1976), Mathematical Theory of Evidence, Princeton, N.J.: Princeton University Press.

    Google Scholar 

  18. S. Y. Sohn and S. H. Lee (2003), “Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea”, Safety Science, 41, 1–14.

    Article  Google Scholar 

  19. M. Sonmez, J. B. Yang and G. D. Holt (2001), “Addressing the contractor selection problem using an evidential reasoning approach”, Engineering Construction and Architectural Management, 8 (3), 198–210.

    Google Scholar 

  20. R. P. Srivastava (1995), “The belief-function approach to aggregating audit evidence”, International Journal of Intelligent Systems, 10 (3), 329–356.

    Article  Google Scholar 

  21. R. P. Srivastava and G. R. Shafer (1992), “Belief-function formulas for audit risk”, The Accounting Review, 67 (2), 249–283.

    Google Scholar 

  22. E. Triantaphyllou (2000), Multi-criteria Decision Making Methods: a Comparative Study, Kluwer Academic Publishers.

    Google Scholar 

  23. J. Wang, H. S. Sii, A. Pillay, J. B. Yang, S. Kim and A. Saajedi (2004), “Use of advances in technology in marine risk assessment”, Risk Analysis, 24 (4), 1011–1033.

    Article  Google Scholar 

  24. J. Wang, J. B. Yang and P. Sen (1996), “Multi-person and multi-attribute design evaluations using evidential reasoning based on subjective safety and cost analysis”, Reliability Engineering and System Safety, 52, 113–127.

    Article  Google Scholar 

  25. X. L. Xie, D. L. Xu, J. B. Yang, J. Wang, J. Ren and S. Yu (2007), “Ship selection using a multiple criteria synthesis approach”, Journal of Marine Science and Technology, in press.

    Google Scholar 

  26. X. Xie, J. B. Yang, D. L. Xu, and A. K. Maddulapalli (2007), “An investigation into multiple criteria evaluation of consumer preferences under uncertainty”, Submitted to 19th International Conference on Multiple Criteria Decision Making, Auckland, New Zealand.

    Google Scholar 

  27. D. L. Xu, J. Liu, J. B. Yang, G. P. Liu, J. Wang, I. Jenkinson and J. Ren (2007), “Inference and learning methodology of belief-rule-based expert system for pipeline leak detection”, Expert Systems with Applications, 32 (1), 103–113.

    Article  Google Scholar 

  28. D. L. Xu, G. McCarthy and J. B. Yang (2006), “Intelligent decision system and its application in business innovation self assessment”, Decision Support Systems, 42, 664–673.

    Article  Google Scholar 

  29. J. B. Yang (2001), “Rule and utility based evidential reasoning approach for multiple attribute decision analysis under uncertainty”, European Journal of Operational Research, 131, 31–61.

    Article  MathSciNet  MATH  Google Scholar 

  30. J. B. Yang, B. G. Dale and C. H. R. Siow (2001), “Self-assessment of excellence: an application of the evidential reasoning approach”, International Journal of Production Research, 39 (16), 3789–3812.

    Article  MATH  Google Scholar 

  31. J. B. Yang and D.L. Xu (2002a), “On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty”, IEEE Transactions on Systems, Man and Cybernetics Part A: Systems and Humans, 32, 289–304.

    Article  Google Scholar 

  32. J. B. Yang and D. L. Xu (2002b), “Nonlinear information aggregation via evidential reasoning in multiple attribute decision analysis under uncertainty”, IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 32, 376–393.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dong-Ling Xu .

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Atlantis Press/World Scientific

About this chapter

Cite this chapter

Xu, DL. (2010). A Web Based Assessment Tool via the Evidential Reasoning Approach. In: Computational Intelligence in Complex Decision Systems. Atlantis Computational Intelligence Systems, vol 2. Atlantis Press. https://doi.org/10.2991/978-94-91216-29-9_7

Download citation

Publish with us

Policies and ethics

Societies and partnerships