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

Michael Doumpos Constantin Zopounidis Emilios C. Galariotis 1
1 Audencia Recherche
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.
Keywords : Multiple criteria analysis Robustness Disaggregation analysis Monte Carlo simulation
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Article dans une revue
European Journal of Operational Research, Elsevier, 2014, 236 (2), pp.601-611. 〈10.1016/j.ejor.2013.12.034〉
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https://hal-audencia.archives-ouvertes.fr/hal-00961323
Contributeur : Galariotis Emilios <>
Soumis le : mercredi 19 mars 2014 - 18:26:48
Dernière modification le : vendredi 21 mars 2014 - 08:14:07

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AUDENCIA | UNAM

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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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