Growing a tree classifier with imprecise data

Symbolic data analysis proposes a general framework to extend usual data an alysis 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 devel oped for probabilistically imprecise data. The new algorithm is presented a s a procedure for extracting knowledge from data of a more general type tha n standard data. Two data sets, respectively, based on categorical and cont inuous variables, are treated in detail. (C) 2000 Elsevier Science B.V. All rights reserved.