C4.5: programs for machine learning
C4.5: programs for machine learning
A statistical approach to decision tree modeling
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Automatic Learning Techniques in Power Systems
Automatic Learning Techniques in Power Systems
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
A Recursive Partitioning Decision Rule for Nonparametric Classification
IEEE Transactions on Computers
Some Enhencements of Decision Tree Bagging
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Using Resampling Techniques for Better Quality Discretization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Dynamic successive feed-forward neural network for learning fuzzy decision tree
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Environmental Modelling & Software
Artificial Intelligence in Medicine
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This paper focuses on the variance introduced by the discretization techniques used to handle continuous attributes in decision tree induction. Different discretization procedures are first studied empirically, then means to reduce the discretization variance are proposed. The experiment shows that discretization variance is large and that it is possible to reduce it significantly without notable computational costs. The resulting variance reduction mainly improves interpretability and stability of decision trees, and marginally their accuracy.