Some remarks about the usage of asymmetric correlation measurements for the induction of decision trees

Decision trees are used very successfully for the identification resp. classification task of objects in many domains like marketing (e.g. Decker, Temme (2001)) or medicine. Other procedures to classify objects are for instance the logistic regression, the logit- or probit analysis, the linear or squared discriminant analysis, the nearest neighbour procedure or some kernel density estimators. The common aim of all these classification procedures is to generate classification rules which describe the correlation between some independent exogenous variables resp. attributes and at least one endogenous variable, the so called class membership variable.