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

Michael Doumpos, Zopounidis Konstantinos, Galariotis, Emilios

Πλήρης Εγγραφή

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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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