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Regularized estimation for preference disaggregation in multiple criteria decision making

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Abstract

Disaggregation methods have been extensively used in multiple criteria decision making to infer preferential information from reference examples, using linear programming techniques. This paper proposes simple extensions of existing formulations, based on the concept of regularization which has been introduced within the context of the statistical learning theory. The properties of the resulting new formulations are analyzed for both ranking and classification problems and experimental results are presented demonstrating the improved performance of the proposed formulations over the ones traditionally used in preference disaggregation analysis.

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Authors and Affiliations

  1. Dept. of Production Engineering and Management, Financial Engineering Laboratory, Technical University of Crete, University Campus, 73100, Chania, Greece

    Michael Doumpos & Constantin Zopounidis

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  1. Michael Doumpos
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  2. Constantin Zopounidis
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Correspondence to Michael Doumpos.

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Doumpos, M., Zopounidis, C. Regularized estimation for preference disaggregation in multiple criteria decision making. Comput Optim Appl 38, 61–80 (2007). https://doi.org/10.1007/s10589-007-9037-9

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  • DOI: https://doi.org/10.1007/s10589-007-9037-9

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