Abstract
Segmentation problems constitute a major part of real world decisions, where a set of alternative actions (solutions) must be classified into two or more predefined classes. The multicriteria decision aid (MCDA) provides several methodologies which are well adapted in segmentation problems. A well known approach in MCDA is based on preference disaggregation which has already been used in ranking problems, but it is also applicable in segmentation problems. The UTADIS (UTilités Additives DIScriminantes) method, a variant of the UTA method, based on the preference disaggregation approach estimates a set of additive utility functions and utility profiles using linear programming techniques in order to minimize the misclassification error between the predefined classes in segmentation problems. This paper presents the application of the UTADIS method in two real world classification problems concerning the field of financial distress. The applications are derived by the studies of Slowinski and Zopounidis (1995), and Dimitras et al. (1996a). The obtained results depict the superiority of the UTADIS method over the discriminant analysis, and they, are also comparable with the results derived by other multicriteria methods.
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Zopounidis, C., Doumpos, M. (1997). Preference Disaggregation Methodology in Segmentation Problems: The Case of Financial Distress. In: Zopounidis, C. (eds) New Operational Approaches for Financial Modelling. Contributions to Management Science. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-59270-6_31
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DOI: https://doi.org/10.1007/978-3-642-59270-6_31
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