C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial Intelligence
Machine Learning
Classification with Belief Decision Trees
AIMSA '00 Proceedings of the 9th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
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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 ordr 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.