Fuzzy decision trees as a decision-making framework in the public sector

Authors

  • Jože Benčina Faculty of Administration University of Ljubljana, Ljubljana, Slovenia

DOI:

https://doi.org/10.2298/YJOR1102205B

Keywords:

Decision tree, appraisal tree, fuzzy set, decision-making, public sector

Abstract

Systematic approaches to making decisions in the public sector are becoming very common. Most often, these approaches concern expert decision models. The expansion of the idea of the development of e-participation and e-democracy was influenced by the development of technology. All stakeholders are supposed to participate in decision making, so this brings a new feature to the decision-making process, in which amateurs and non-specialists are participating decision making instead of experts. To be able to understand the needs and wishes of stakeholders, it is not enough to vote for alternatives - it is important to participate in solution-finding and to express opinions about the important elements of these matters. The solution presented in this paper concerns fuzzy decision-making framework. This framework combines the advantages of the introduction of the decision-making problem in a tree structure and the possibilities offered by the flexibility of the fuzzy approach. The possibilities of implementation of the framework in practice are introduced by case studies of investment projects appraisal in a community and assessment of efficiency and effectiveness of public institutions.

References

Benčina, J., "The use of fuzzy logic in coordinating investment projects in the public sector", The Proceedings of Rijeka Faculty of Economics – Journal of Economics and Business, 25 (1) (2007) 113-136.

Bohanec, M., Zupan, B., and Rajkovič, V., "Applications of qualitative multi-attribute decision models in health care", International Journal of Medical Informatics, 58-59 (2000) 191–205.

Bonissone, P. P., and Decker, K. S., "Selecting uncertainty calculi and granularity: an experiment in trading-off precision and complexity", in: Kanal, L. N., Lemmer, J. F. (eds.) Uncertainty in Artificial Intelligence, Machine Intelligence and Pattern Recognition, Elsevier Science Publisher B.V., Amsterdam 4 (1986) 217–247.

Bots, P. W. G., and Lootsma, F. A., "Decision support in the public sector", Journal of Multi-Criteria Decision Analysis, 9 (1-3) (2000) 1-6.

Chen, L. H., and Chiou, T. W., "A fuzzy credit-rating approach for commercial loans - a Taiwan case", Omega, International Journal of Management Science, 27 (1999) 407–419.

Chen, S. M.: "Fuzzy group decision making for evaluating the rate of aggregative risk in software development", Fuzzy Sets and Systems, 118 (2001) 75–88.

Chen, R. Y., Sheu, D. D., and Liu, C. M., "Vague knowledge search in the design for outsourcing using fuzzy decision tree", Computers & Operations Research, 34 (2007) 3628-3637.

Dale, M. B., Dale. P. E. R., and Tan, P., "Supervised clustering using decision trees and decision graphs: An ecological comparison", Ecological Modelling, 204 (1–2) (2007) 70–78.

Gammack, J., and Barker M., E-Democracy and Public Participation: A Global Overview of Policy and Activity, School of Management, Griffith University Queensland, 2003.

Gasar, S., Bohanec, M., and Rajkovič, V., "Combined data mining and decision support approach to the prediction of academic achievement", in: Bohanec, M. et al. (eds.) ECML/PKDD'02 workshop on Integrating Aspects of Data Mining, Decision Support and Meta-Learning, University of Helsinki, 2002, 41–52.

Geissena, V., Kampichlerb, C., López-de Llergo-Juáreza, J. J., and Galindo-Acántara, A., "Superficial and subterranean soil erosion in Tabasco, tropical Mexico: Development of a decision tree modeling approach", Geoderma, 139 (3–4) (2007) 277–287.

GAO/AIMD-98-110, Leading Practices in Capital Decision-Making, Washington: U.S. General Accounting Office, 1998.

Harper, P. R., and Winslett, D. J., "Classification trees: A possible method for maternity risk grouping", European Journal of Operational Research, 169 (1) (2006) 146–156.

Hsieh, T.Y., Lu, S.T., and Tzeng, G.-H., "Fuzzy MCDM approach for planning and design tenders selection in public office buildings", International Journal of Project Management, 22(7) (2004) 573-584.

Huang, W.C., Teng, J.Y. & Lin, M.C., "Application of fuzzy multiple criteria decision making in the selection of infrastructure projects", IN: Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008 IEEE, 159-163.

Jereb, E., Rajkovič, U., and Rajkovič, V., "A hierarchical multi-attribute system approach to personnel selection", International Journal of Selection and Assessment, 13 (3) (2005) 198-205.

Keefer, D. L., Kirkwood, C. W., and Corner, J. L., “Summary of decision analysis applications in the operations research literature”, 1990–2001, Technical Report, Department of Supply Chain Management, Arizona State University, Tempe, Arizona, 2002.

Lai, W. H., Chang, P. L., and Chou, Y. C., "Fuzzy MCDM approach to R&D project evaluation in Taiwan’s public sectors", Journal of Technology Management in China, 5 (2010) 84-101.

Lo, K. L., Abidin, H. I. H. Z., "The fuzzy decision tree application to a power system problem", The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 23 (2) (2004) 436–451.

Metaxiotis, K., Psarras J., and Samouilidis E., "Integrating fuzzy logic into decision support systems: current research and future prospects", Information Management & Computer Security, 11 (2) (2003) 53–59.

Niven, P.R., Balanced Scorecard Step-by-step for Government and Nonprofit Agencies, Wiley, Hoboken, New Jersey, 2003.

Kaplan, R.S., and Norton, D.P., The balanced scorecard: translating strategy into action, Harvard Business School Press, Boston, 1996.

Olaru, C., and Wehenkel, L., "A complete fuzzy decision tree technique", Fuzzy Sets and Systems, 138 (2) (2003) 221–254.

Podgorelec, V., Kokol, P., Štiglic, B., and Rozman, I., "Decision trees: an overview and their use in medicine", Journal of Medical Systems, 26 (5) (2002) 445–463.

Proctor, W., and Drechsler, M., "Deliberative multi-criteria evaluation: A case study of recreation and tourism options in Victoria Australia", European Society for Ecological Economics, Frontiers 2 Conference, 2003.

Quinlan, J. R., "Decision trees and decision-making", IEEE Transactions on Systems, Man and Cybernetics, 20 (2) (1990) 339-346.

Sahay, B. S., and Gupta A. K., "Development of software selection criteria for supply chain solutions", Industrial Management & Data Systems, 103 (2) (2003) 97–110.

Sastry, R., and Ranganathan, N., "A VLSI architecture for approximate tree matching", IEEE Transactions on Computers, 47 (3) (1998) 346–352.

Savšek, T., Vezjak M., and Pavešić, N., "Fuzzy trees in decision support systems", European Journal of Operational Research, 174 (1) (2006) 293–310.

Tran, L., and Duckstein, L., "Comparison of fuzzy numbers using a fuzzy distance measure", Fuzzy Sets and Systems, 130 (2002) 331–341.

Verdev, M., Bohanec, M., and Džeroski, S., Decision Support for a Waste Electrical and Electronic Equipment Treatment System, 2006.

Wang, X., and Borgelt, C., "Information measures in fuzzy decision trees," in: Proceedings of 2004 IEEE International Conference on Fuzzy Systems, 1 (2004) 85–90.

Yanikow, C. Z., "Fuzzy Decision Trees: Issues and Methods", IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 28 (1) (1998) 1–14.

Yuan, Y., and Shaw, M. J., "Induction of fuzzy decision trees", Fuzzy Sets and Systems, 69 (2) (1995), 125–139.

Zadeh, L. A., "Fuzzy sets", Information and Control, 8 (1965) 338–353.

Zadeh, L. A., "The concept of a linguistic variable and its application to approximate reasoning", Information Sciences, 8 (1975) 301–357.

Zimmerman, H. J., Fuzzy Set Theory and Its Applications, Kluver Academic Publishers, Boston/Dordrecht/London, 2001.

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Published

2011-09-01

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Research Articles