C4.5: programs for machine learning
C4.5: programs for machine learning
ELEM2: A Learning System for More Accurate Classifications
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
From Computational Intelligence to Web Intelligence: An Ensemble from Potpourri
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
CViz: An Interactive Visualization System for Rule Induction
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
The Needles-in-Haystack Problem
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Dynamic discreduction using Rough Sets
Applied Soft Computing
CD: a coupled discretization algorithm
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Clustering and classifying informative attributes using rough set theory
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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We present a comparison of three entropy-based discretization methods in a context of learning classification rules. We compare the binary recursive discretization with a stopping criterion based on the Minimum Description Length Principle (MDLP)[3], a nonrecursive method which simply chooses a number of cut-points with the highest entropy gains, and a non-recursive method that selects cut-points according to both information entropy and distribution of potential cut-points over the instance space. Our empirical results show that the third method gives the best predictive performance among the three methods tested.