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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Application of E 2 M Decision Trees to Rubber Quality Prediction
verfasst von : Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denœux
Erschienen in: Information Processing and Management of Uncertainty in Knowledge-Based Systems
Verlag: Springer International Publishing
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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.