Implementing imprecision in information systems
Information Sciences: an International Journal - Special issue on expert systems
On the specificity of a possibility distribution
Fuzzy Sets and Systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Machine Learning
Possibilistic Induction in Decision-Tree Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Learning from ambiguously labeled examples
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
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
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This paper investigates an extension of classification trees to deal with uncertain information where uncertainty is encoded in possibility theory framework. Class labels in data sets are no longer singletons but are given in the form of possibility distributions. Such situation may occur in many real-world problems and cannot be dealt with standard decision trees. We propose a new method for assessing the impurity of a set of possibility distributions representing instances's classes belonging to a given training partition. The proposed approach takes into account the mean similarity degree of each set of possibility distributions representing a given training partition. The so-called information closeness index is used to evaluate this similarity. Experimental results show good performance on well-known benchmarks.