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Preference disaggregation and statistical learning for multicriteria decision support: a review

Michael Doumpos, Zopounidis Konstantinos

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URIhttp://purl.tuc.gr/dl/dias/A96C320A-69BE-43E1-AAA7-580D6EAC61E1-
Αναγνωριστικόhttp://www.sciencedirect.com/science/article/pii/S0377221710003851-
Αναγνωριστικόhttps://doi.org/10.1016/j.ejor.2010.05.029-
Γλώσσαen-
Μέγεθος12 pagesen
ΤίτλοςPreference disaggregation and statistical learning for multicriteria decision support: a reviewen
ΔημιουργόςMichael Doumposen
ΔημιουργόςΔουμπος Μιχαληςel
ΔημιουργόςZopounidis Konstantinosen
ΔημιουργόςΖοπουνιδης Κωνσταντινοςel
ΕκδότηςElsevieren
ΠερίληψηDisaggregation methods have become popular in multicriteria decision aiding (MCDA) for eliciting preferential information and constructing decision models from decision examples. From a statistical point of view, data mining and machine learning are also involved with similar problems, mainly with regard to identifying patterns and extracting knowledge from data. Recent research has also focused on the introduction of specific domain knowledge in machine learning algorithms. Thus, the connections between disaggregation methods in MCDA and traditional machine learning tools are becoming stronger. In this paper the relationships between the two fields are explored. The differences and similarities between the two approaches are identified, and a review is given regarding the integration of the two fields.en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-11-09-
Ημερομηνία Δημοσίευσης2011-
Θεματική ΚατηγορίαMultiple criteria analysisen
Θεματική ΚατηγορίαDisaggregation analysisen
Θεματική ΚατηγορίαPreference learningen
Θεματική ΚατηγορίαData miningen
Βιβλιογραφική ΑναφοράM. Doumpos and C. Zopounidis, "Preference disaggregation and statistical learning for multicriteria decision support: a review," Europ. J. Operat. Res., vol. 209, no. 3, pp. 203-214, Mar. 2011. doi:10.1016/j.ejor.2010.05.029en

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