Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Calculating Dempster-Shafer Plausibility
IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial Intelligence
Machine Learning
Possibilistic Induction in Decision-Tree Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Reasoning with imprecise belief structures
International Journal of Approximate Reasoning
Qualitative inference in possibilistic option decision trees
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Decision trees as possibilistic classifiers
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Rule discovery process based on rough sets under the belief function framework
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Classification with dynamic reducts and belief functions
Transactions on rough sets XIV
Classification systems based on rough sets under the belief function framework
International Journal of Approximate Reasoning
Pain monitoring: A dynamic and context-sensitive system
Pattern Recognition
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The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function theory. The uncertainty can appear either in the actual class of training objects or attribute values of objects to classify. From the procedures of building BDT, we mention the averaging and the conjunctive approaches. In this paper, we develop pruning methods of belief decision trees induced within averaging and conjunctive approaches where the objective is to cope with the problem of overfitting the data in BDT in order to improve its comprehension and to increase its quality of the classification.