Classification trees for preference data: a distance-based approach

In the framework of preference rankings, when the interest lies in explaining which predictors and which interactions among predictors are able to explain the observed preference structures, the possibility to derive consensus measures using a classi cation tree represents a novelty and an important tool given its easy interpretability. In this work we propose the use of a multivariate decision tree where a weighted Kemeny distance is used both to evaluate the distances between rankings and to de ne an impurity measure to be used in the recursive partitioning. The proposed approach allows also to weight di erently high distances in rankings in the top and in the bottom alternatives.