Impact of the presence of linguistic data on the decision aid process
Wybrane pełne teksty z tego czasopisma
Many decision situations are described using both numerical and linguistic values. Because of the presence of linguistic data, analytics are forced to apply appropriate measures. These measures are often applied arbitrary, without consulting the Decision Maker, which creates an intangible gap between the DM’s intentions and the final decision model. The paper analyses the impact of applying different conventions forutilising ling uistic values in the decision aiding process. The considered measures include quantification, fuzzy modelling and applying linguistic versions of MCDA methods. Concluding remarks describes advantages of the alignment of the decision aiding process to the Decision Maker’s problem formulation.
Bibliogr. 18 poz., rys., tab.
-  A. Tsoukiàs, On the concept of decision aiding process: an operational perspective, Annals of Operations Research, 154 (2007), pp. 3-27.
-  E. Ertugrul Karsak and E. Tolga, Fuzzy multi-criteria decision-making procedure for evaluating advanced manufacturing system investments, International Journal of Production Economics, 69 (2001), pp. 49-64.
-  S.-L. Chang, R.-C. Wang and S.-Y. Wang, Applying a direct multi-granularity linguistic and strategy-oriented aggregation approach on the assessment of supply performance, European Journal of Operational Research, 177 (2007), pp. 1013-1025.
-  J. Malczewski and C. Rinner, Exploring multicriteria decision strategies in GIS with linguistic quantifiers: A case study of residential quality evaluation, Journal of Geographical Systems, 7 (2005), pp. 249-268.
-  V. A. Niskanen, A soft multi-criteria decision-making approach to assessing the goodness of typical reasoning systems based on empirical data, Fuzzy Sets and Systems, 131 (2002), pp. 79-100.
-  A. Piegat, Are Linguistic Evaluations Used by People of Possibilistic or Probabilistic Nature?, in J. G. a. S. Carbonell, J., ed., Artificial Intelligence and Soft Computing - ICAISC 2004, Springer, Berlin / Heidelberg, 2004, pp. 356-363.
-  H. Moshkovich, A. Mechitov and D. Olson, Verbal Decision Analysis, Multiple Criteria Decision Analysis: State of the Art Surveys, 2005, pp. 609-633.
-  J. Ma, D. Ruan, Y. Xu and G. Zhang, A fuzzy-set approach to treat determinacy and consistency of linguistic terms in multi-criteria decision making, International Journal of Approximate Reasoning, 44 (2007), pp. 165-181.
-  Zadeh, Precisiated Natural Language, Aspects of Automatic Text Analysis, 2006.
-  F. Herrera and E. Herrera-Viedma, Linguistic decision analysis: steps for solving decision problems under linguistic information, Fuzzy Sets Syst., 115 (2000), pp. 67-82.
-  H. Mangassarian and H. Artail, A general framework for subjective information extraction from unstructured English text, Data & Knowledge Engineering, 62 (2007), pp. 352-367.
-  A. Imsombut and A. Kawtrakul, Automatic building of an ontology on the basis of text corpora in Thai, Language Resources and Evaluation.
-  B. Roy, Paradigms and Challenges, Multiple Criteria Decision Analysis: State of the Art Surveys, 2005, pp. 3-24.
-  M. S. Garcia-Cascales and M. T. Lamata, Solving a decision problem with linguistic information, Pattern Recognition Letters, 28 (2007), pp. 2284-2294.
-  Zadeh, Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems, Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2 (1998), pp. 23-25.
-  Đ. Ertuğrul and M. Günes, Fuzzy Multi-criteria Decision Making Method for Machine Selection, Analysis and Design of Intelligent Systems using Soft Computing Techniques, 2007, pp. 638-648.
-  V.-N. Huynh and Y. Nakamori, Multi-Expert Decision-Making with Linguistic Information: A Probabilistic-Based Model, Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 3 - Volume 03 (2005), pp. 91.3.
-  M. Nikravesh and D.-Y. Choi, Soft Computing for Perception Based Information Processing, Soft Computing for Information Processing and Analysis, 2005, pp. 203-255.