Classification trees for preference data: a distance-based approach

Research output: Contribution to conferenceOtherpeer-review


In the framework of preference rankings, when the interest lies inexplaining which predictors and which interactions among predictors are ableto explain the observed preference structures, the possibility to derive consensusmeasures using a classi cation tree represents a novelty and an important toolgiven its easy interpretability. In this work we propose the use of a multivariatedecision tree where a weighted Kemeny distance is used both to evaluate thedistances between rankings and to de ne an impurity measure to be used in therecursive partitioning. The proposed approach allows also to weight di erentlyhigh distances in rankings in the top and in the bottom alternatives.
Original languageEnglish
Number of pages4
Publication statusPublished - 2014


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