The comparison between classification trees through proximity measures
Several proximity measures have been proposed to compare classifications derived from different clustering algorithms. There are few proposed solutions for the comparison of two classification trees; some of them measure the difference between the structures of the trees, some other compare the partitions associated to the trees taking into account their predictive power. Their features and limitations are discussed. Furthermore, a new dissimilarity measure is proposed; it considers both the aspects explored separately by the previous ones. Three of these measures are then compared analyzing two different classification problems: a real data set and a simulation study. With respect to the real data set it is also evaluated how and how much each of the considered measures is influenced by the presence of highly predictive variables which are also highly correlated.
classification tree proximity measure partition tree topology