Abstract
Dominance-based Rough Set Approach (DRSA) has been proposed for multi-criteria classification problems in order to handle inconsistencies in the input information with respect to the dominance principle. The end result of DRSA is a decision rule model of Decision Maker preferences. In this paper, we consider an additive function model resulting from dominance-based rough approximations. The presented approach is similar to UTA and UTADIS methods. However, we define a goal function of the optimization problem in a similar way as it is done in Support Vector Machines (SVM). The problem may also be defined as the one of searching for linear value functions in a transformed feature space obtained by exhaustive binarization of criteria.
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Dembczyński, K., Kotłowski, W., Słowiński, R. (2006). Additive Preference Model with Piecewise Linear Components Resulting from Dominance-Based Rough Set Approximations. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_53
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DOI: https://doi.org/10.1007/11785231_53
Publisher Name: Springer, Berlin, Heidelberg
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