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

Michael Doumpos, Zopounidis Konstantinos, Galariotis, Emilios

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URIhttp://purl.tuc.gr/dl/dias/BC4155A9-6DD1-4FFB-ABC8-620B28F0C858-
Identifierhttp://www.sciencedirect.com/science/article/pii/S0377221713010187-
Identifierhttps://doi.org/10.1016/j.ejor.2013.12.034-
Languageen-
Extent11 pagesen
TitleInferring robust decision models in multicriteria classification problems: an experimental analysisen
CreatorMichael Doumposen
CreatorΔουμπος Μιχαληςel
CreatorZopounidis Konstantinosen
CreatorΖοπουνιδης Κωνσταντινοςel
CreatorGalariotis, Emiliosen
PublisherElsevieren
Content SummaryRecent 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.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-09-
Date of Publication2014-
SubjectMultiple criteria analysisen
SubjectRobustnessen
SubjectDisaggregation analysisen
SubjectMonte Carlo simulationen
Bibliographic CitationM. 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.2013.12.034en

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