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 E 2 M 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 E 2 M decision trees are presented in order to improve the interpretation of the tree compared to standard decision trees. © Springer International Publishing Switzerland 2014.
CITATION STYLE
Sutton-Charani, N., Destercke, S., & Denœux, T. (2014). Application of E2M Decision Trees to Rubber Quality Prediction. In Communications in Computer and Information Science (Vol. 442 CCIS, pp. 107–116). Springer Verlag. https://doi.org/10.1007/978-3-319-08795-5_12
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