Symbolic object description of strata by segmentation trees.

Abstract

Based in a,generalised recursive tree-building algorithm for populations partitioned into strata a method to obtain simple descriptions of strata is presented. Also strata with a common rule are obtained. Common predictors and criterion variable describe population in all strata or classes of individuals. Algorithm considers strata structure in tree-building algorithm and combines in each step maximisation of an information content measure for the criterion variable in a new binary partition of the population and selection of decisional nodes, based in quality of prediction for subsets of strata. Each decisional tree node is composed of a set of strata and a rule for individuals in these strata that will jointly explain the criterion variable. Symbolic data analysis fits the method. Input of the algorithm is composed of classes of individuals. Algorithm is extended to individuals described by probabilistic symbolic objects. As output, symbolic objects describe tree, decisional nodes and strata.