Abstract
In this paper, we propose a method based on the belief decision tree approach, to classify scenarios in an uncertain context. Our method uses both the decision tree technique and the belief function theory as understood in the transferable belief model in order to find the classes of the scenarios (of a given problem) that may happen in the future. Two major phases will be ensured: the construction of the belief decision tree representing the scenarios belonging to the training set and which may present some uncertainty in their class membership, this uncertainty is presented by belief functions. Then, the classification of new scenarios characterized generally by uncertain hypotheses’ configurations.
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Elouedi, Z., Mellouli, K. (2000). Classifying Scenarios using Belief Decision Trees. In: Arikawa, S., Morishita, S. (eds) Discovery Science. DS 2000. Lecture Notes in Computer Science(), vol 1967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44418-1_11
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DOI: https://doi.org/10.1007/3-540-44418-1_11
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