Modelling for Recursive Partitioning and Variable Selection
Roberta Siciliano, Francesco Molà · COMPSTAT · 1994
We present a binary segmentation methodology in which it is possible to select simultaneously sub-groups of variables as well as sub-groups of cases in each node of the binary tree. To a recursive partition procedure which defines either a classification tree or a regression tree we add a hierarchy of models. The main advantages of this approach, especially for large samples, are: to abandon immediately unsignificant variables; to reduce rapidly the number of possible splits in each node; the amalgamation procedure becomes faster and is of higher interpretative value.