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Inferring robust decision models in multicriteria classification problems: An experimental analysis

Abstract : Recent research on robust decision aiding has focused on identifying a range of recommendations from preferential information and the selection of representative models compatible with preferential constraints. This study presents an experimental analysis on the relationship between the results of a single decision model (additive value function) and the ones from the full set of compatible models in classification problems. Different optimization formulations for selecting a representative model are tested on artificially generated data sets with varying characteristics.
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https://hal-audencia.archives-ouvertes.fr/hal-00961323
Contributor : Galariotis Emilios <>
Submitted on : Wednesday, March 19, 2014 - 6:26:48 PM
Last modification on : Friday, July 26, 2019 - 11:58:03 AM

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Michael Doumpos, Constantin Zopounidis, Emilios C. Galariotis. Inferring robust decision models in multicriteria classification problems: An experimental analysis. European Journal of Operational Research, Elsevier, 2014, 236 (2), pp.601-611. ⟨10.1016/j.ejor.2013.12.034⟩. ⟨hal-00961323⟩

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