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.
symbolic data analysis probabilistically imprecise data classification tree supervised learning soft recursive partition