Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Karami, Amin; Johansson, Ronnie
Publisher: Institute of Information Science Academia Sinica
Languages: English
Types: Article
An information fusion system with local sensors sometimes requires the capability\ud to represent the temporal changes of uncertain sensory information in dynamic and uncertain\ud situation to access to a hypothesis node which cannot be observed directly. One\ud of the central issue and challenging problem is the decision of what combination and order\ud of sensors allocation should be selected between sensors, in order to maximize the\ud global gain in the flow of information, when the data association is limited. In this area,\ud Bayesian Networks (BNs) can constitute a coherent fusion structure and introduce different\ud options (the combination of sensors allocation) for achieving to the hypothesis\ud node through a number of intermediate nodes that are interrelated by cause and effect.\ud BNs can rank the options in terms of their probabilities from Bayes’ theorem calculation.\ud But, decision making based on probabilities and numerical representations might not be\ud appropriate. Thus, re-ranking the set of options based on multiple criteria such as those\ud of multi-criteria decision aid (MCDA) should be ideally considered. Re-ranking and selecting\ud the appropriate options are considered as a multi-attribute decision making\ud (MADM) problem by user interaction as semi-automatically decision support. In this\ud paper, Multi Attribute Decision Making (MADM) techniques as TOPSIS, SAW, and\ud Mixed (Rank Average) for decision-making as well as AHP and Entropy for obtaining\ud the weights of attributes have been used. Since MADM techniques give most probably\ud different results according to different approaches and assumptions in the same problem,\ud statistical analysis done on them. According to the results, the correlation between compared\ud techniques for re-ranking BN options is strong and positive because of the close\ud proximity of weights suggested by AHP and Entropy. Mixed method as compared to\ud TOPSIS and SAW is the preferred technique when there is no historical (real) decision-making\ud case; moreover, AHP is more acceptable than Entropy for weighting.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. H. Boström, S. F. Andler, M. Brohede, R. Johansson, A. Karlsson, J. van Laere, L. Niklasson, M. Nilsson, A. Persson, and T. Ziemke, “On the definition of information fusion as a field of research,” Technical Report, HS-IKI-TR-07-006, School of Humanities and Informatics, University of Skövde, Sweden, 2007.
    • 2. T. J. Stevens and M. K. Sundareshan, “Probabilistic neural network-based sensor configuration management in a wireless ad-hoc network,” Department of Electrical and Computer Engineering, University of Arizona, Tucson, 2004.
    • 3. E. Bossé, J. Roy, and S. Wark, Concepts, Models, and Tools for Information Fusion, Artech House Inc., Norwood, MA, 2007.
    • 4. N. Fenton and M. Neil, “Making decisions: using Bayesian nets and MCDA,” Knowledge-Based Systems, Vol. 14, 2001, pp. 307-325.
    • 5. E. Besada-Portas, J. A. Lopez-Orozco, and J. M. de la Cruze, “Unified fusion system based on Bayesian networks for autonomous mobile robots,” in Proceedings of the 5th International Conference on Information Fusion, 2002, pp. 873-880.
    • 6. M. Nilsson and T. Ziemke, “Information fusion: A decision support perspective,” in Proceedings of the International Conference on Information Fusion, 2007, pp. 1-8.
    • 7. M. Pirdashti, A. Ghadi, M. Mohammadi, and G. Shojatalab, “Multi-criteria decisionmaking selection model with application to chemical engineering management decisions,” in Proceedings of World Academy of Science, Engineering and Technology, Vol. 49, 2009, pp. _____.
    • 8. K. Devi, S. P. Yadav, and S. Kumar, “Extension of fuzzy TOPSIS method based on vague sets,” Computational Cognition, Vol. 7, 2009, pp. 58-62.
    • 9. S. K. Cheng, “Development of a fuzzy multi-criteria decision support system for municipal solid waste management,” Master Thesis, Applied Science in Advanced Manufacturing and Production Systems, University of Regina, Canada, 2000.
    • 10. C. Yeh, “A problem-based selection of multi-attribute decision making methods,” International Transactions in Operational Research, Vol. 9, 2002, pp. 169-181.
    • 11. H. Soltanpanah, H. Farughi, and M. Golabi, “Utilization and comparison of multi attribute decision techniques to rank countries upon human development rate,” International Research Journal of Finance and Economics, Issue 60, 2010, pp. 175-188.
    • 12. E. Triantaphyllou, Multi-attribute Decision Making Methods: A Comparative Study, Kluwer Academic Publishers, ____(place), 2000.
    • 13. J. Lezzi, “Multi-criteria decision making in outpatient scheduling,” Master Thesis, Department of __________, University of South Florida, USA, 2006.
    • 14. M. Janic and A. Reggiani, “An application of the multiple criteria decision making (MCDM) analysis to the selection of a new hub airport,” European Journal of Transport and Infrastructure Research, Vol. 2, 2002, pp. 113-142.
    • 15. K. Yoon and C. L. Hwang, Multiple Attribute Decision Making Methods and Applications, Springer Verlag, ____(place), 1981.
    • 16. L. Jorge, A. García, M. G. Ibarra, and P. L. Rico, “Improvement of TOPSIS technique through integration of Malahanobis distance: A case study,” in Proceedings of the 14th Annual International Conference on Industrial Engineering Theory, Applications and Practice, 2009, pp. 135-141.
    • 17. T. L. Saaty, The Analytic Hierarchy Process, McGraw Hill International, 1980.
    • 18. G. Coyle, The Analytic Hierarchy Process (AHP), Practical Strategy, Open Access Material, Pearson Education Limited, 2004.
    • 19. H. Ariff, M. S. Salit, N. Ismail, and Y. Nukman, “Use of analytical hierarchy process (AHP) for selecting the best design concept,” Jurnal Teknologi, Vol. 49(A) Dis., 2008, pp. 1-18.
    • 20. M. E. Andreica, I. Dobre, M. I. Andreica, and C. Resteanu, “A new portfolio selection method based on interval data,” Studies in Informatics and Control, Vol. 19, 2010, pp. 253-262.
    • 21. W. Watthayu and Y. Peng, “A Bayesian network based framework for multi-criteria decision making,” in Proceedings of the 17th International Conference on Multiple Criteria Decision Analysis, 2004, pp. ______.
    • 22. J. Li and J. Jin, “Optimal sensor allocation by integrating causal models and setcovering algorithms,” IIE Transactions, Vol. 42, 2010, pp. 564-576.
    • 23. F. Jensen, Bayesian Networks and Decision Graphs, Springer-Verlag, ____(place), 2002.
    • 24. F. Saidi, O. Stasse, and K. Yokoi, “A visual attention framework for search behavior by a humanoid robot,” in Proceedings of the 6th IEEE-RAS International Conference on Humanoid Robots, 2006, pp. 346-351.
    • 25. W. Premchaiswadi and N. Jongsawat, “Group decision making using Bayesian network inference with qualitative expert knowledge,” in Proceedings of the 5th IEEE International Conference on Intelligent Systems, 2010, pp. 126-131.
    • 26. J. Jamieson, “Information systems decision making: factors affecting decision makers and outcomes,” Ph.D. Thesis, Department of Business and Informatics, Central Queensland University Rockhampton, Australia, 2007.
    • 27. R. Johansson and C. Martenson, “Information acquisition strategies for Bayesian network-based decision support,” in Proceedings of the 13th International Conference on Information Fusion, 2010, pp. 1-8.
    • 28. G. A. Mendoza, P. Macoun, R. Prabhu, D. Sukadri, H. Purnomo, and H. Hartanto, Guidelines for Applying Multi-Criteria Analysis to the Assessment of Criteria and Indicators, Center for International Forestry Research, Jakarta.
    • 29. R. Simanaviciene and L. Ustinovichius, “Sensitivity analysis for quantitative decision making methods: TOPSIS and SAW,” in Proceedings of the 16th International Conference on Information and Software Technologies, 2010, pp. ______.
    • 30. E. Stevens-Navarro and V. W. S. Wong, “Comparison between vertical handoff decision algorithms for heterogeneous wireless networks,” in Proceedings of the 63rd IEEE Vehicular Technology Conference, Vol. 2, 2006, pp. 947-951.
    • 31. A. P. Agalgaonkar, S. V. Kulkarni, and S. A. Khaparde, “Multi-attribute decision making approach for strategic planning of DGs,” IEEE Power Engineering Society General Meeting, 2005, pp. 2985-2990. Amin Karami received the MSc degree in Informatics field from University of Skövde, Sweden in 2011. He is currently Ph.D. student at the Universitat Politècnica de Catalunya Barcelona Tech (UPC), Spain. His current research interests include computational intelligence, optimization techniques, information security, and content-centric networks.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article