Belief decision trees: theoretical foundations

AbstractThis paper extends the decision tree technique to an uncertain environment where the uncertainty is represented by belief functions as interpreted in the transferable belief model (TBM). This so-called belief decision tree is a new classification method adapted to uncertain data. We will be concerned with the construction of the belief decision tree from a training set where the knowledge about the instances' classes is represented by belief functions, and its use for the classification of new instances where the knowledge about the attributes' values is represented by belief functions