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

Michael Doumpos, Zopounidis Konstantinos, Galariotis, Emilios

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/BC4155A9-6DD1-4FFB-ABC8-620B28F0C858
Έτος 2014
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά M. Doumpos, C. Zopounidis and E. Galariotis, "Inferring robust decision models in multicriteria classification problems: an experimental analysis," Europ. J. Operat. Res., vol. 236, no. 2, pp. 601-611, Jul. 2014. doi:10.1016/j.ejor.2013.12.034 https://doi.org/10.1016/j.ejor.2013.12.034
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Περίληψη

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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