Additive Preference Model with Piecewise Linear Components Resulting from Dominance-Based Rough Set Approximations

  • Krzysztof Dembczyński
  • Wojciech Kotłowski
  • Roman Słowiński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


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.


Support Vector Machine Decision Table Decision Class Marginal Function Wisconsin Breast Cancer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Krzysztof Dembczyński
    • 1
  • Wojciech Kotłowski
    • 1
  • Roman Słowiński
    • 1
    • 2
  1. 1.Institute of Computing SciencePoznań University of TechnologyPoznańPoland
  2. 2.Polish Academy of SciencesInstitute for Systems ResearchWarsawPoland

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