C4.5: programs for machine learning
C4.5: programs for machine learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Foundations of Fuzzy Systems
Machine Learning
Possibilistic Induction in Decision-Tree Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Decision trees as possibilistic classifiers
International Journal of Approximate Reasoning
Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Learning from ambiguously labeled examples
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
SIM-PDT: a similarity based possibilistic decision tree approach
FoIKS'08 Proceedings of the 5th international conference on Foundations of information and knowledge systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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: a recent overview
Artificial Intelligence Review
| Hi-index | 0.01 |
This paper presents an extension of a standard decision tree classifier, namely, the C4.5 algorithm. This extension allows the C4.5 algorithm to handle uncertain labeled training data where uncertainty is modeled within the possibility theory framework. The extension mainly concerns the attribute selection measure in which a clustering of possibility distributions of a partition is performed in order to assess the homogeneity of that partition. This paper also provides a comparison with previously proposed possibilistic decision tree approaches.