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Learning non-monotonic additive value functions for multicriteria decision making

Abstract

Multiattribute additive value functions constitute an important class of models for multicriteria decision making. Such models are often used to rank a set of alternatives or to classify them into pre-defined groups. Preference disaggregation techniques have been used to construct additive value models using linear programming techniques based on the assumption of monotonic preferences. This paper presents a methodology to construct non-monotonic value function models, using an evolutionary optimization approach. The methodology is implemented for the construction of multicriteria models that can be used to classify the alternatives in pre-defined groups, with an application to credit rating.

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Correspondence to Michael Doumpos.

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Doumpos, M. Learning non-monotonic additive value functions for multicriteria decision making. OR Spectrum 34, 89–106 (2012). https://doi.org/10.1007/s00291-010-0231-2

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  • DOI: https://doi.org/10.1007/s00291-010-0231-2

Keywords

  • Multicriteria decision making
  • Value functions
  • Evolutionary optimization
  • Classification