Growing a tree classifier with imprecise data

Symbolic data analysis proposes a general framework to extend usual data analysis methods to more complex data called symbolic objects. The prediction problem for symbolic objects is defined: it is seen to be a generalization of the prediction for standard data. An algorithm of tree-growing is developed for probabilistically imprecise data. The new algorithm is presented as a procedure for extracting knowledge from data of a more general type than standard data. Two data sets, respectively, based on categorical and continuous variables, are treated in detail.