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

Michael Doumpos, Zopounidis Konstantinos

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/A96C320A-69BE-43E1-AAA7-580D6EAC61E1
Έτος 2011
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
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Βιβλιογραφική Αναφορά M. 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.2010.05.029 https://doi.org/10.1016/j.ejor.2010.05.029
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Περίληψη

Disaggregation 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.

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