Application of E 2 M Decision Trees to Rubber Quality Prediction

In many applications, data are often imperfect, incomplete or more generally uncertain. This imperfection has to be integrated into the learning process as an information in itself. The E2M decision trees is a methodology that provides predictions from uncertain data modelled by belief functions. In this paper, the problem of rubber quality prediction is presented with a belief function modelling of some data uncertainties. Some resulting E2M decision trees are presented in order to improve the interpretation of the tree compared to standard decision trees.