C4.5: programs for machine learning
C4.5: programs for machine learning
Lazy learning
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Option Decision Trees with Majority Votes
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Fuzzy set-based methods in instance-based reasoning
IEEE Transactions on Fuzzy Systems
Pruning belief decision tree methods in averaging and conjunctive approaches
International Journal of Approximate Reasoning
Decision trees as possibilistic classifiers
International Journal of Approximate Reasoning
On the Use of Clustering in Possibilistic Decision Tree Induction
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Fuzzy methods in machine learning and data mining: Status and prospects
Fuzzy Sets and Systems
SIM-PDT: a similarity based possibilistic decision tree approach
FoIKS'08 Proceedings of the 5th international conference on Foundations of information and knowledge systems
Uncertainty in clustering and classification
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Fuzzy sets in machine learning and data mining
Applied Soft Computing
Qualitative inference in possibilistic option decision trees
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Inference in possibilistic network classifiers under uncertain observations
Annals of Mathematics and Artificial Intelligence
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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We propose a generalization of Ockham's razor, a widely applied principle of inductive inference. This generalization intends to capture the aspect of uncertainty involved in inductive reasoning. To this end, Ockham's razor is formalized within the framework of possibility theory: It is not simply used for identifying a single, apparently optimal model, but rather for concluding on the possibility of various candidate models. The possibilistic version of Ockham's razor is applied to (lazy) decision tree learning.