Relative Unsupervised Discretization for Regresseion Problems
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Utility-Based Decision Tree Optimization: A Framework for Adaptive Interviewing
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Moving towards efficient decision tree construction
Information Sciences: an International Journal
A new node splitting measure for decision tree construction
Pattern Recognition
Towards the automatic design of decision tree induction algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Feature selection method using preferences aggregation
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Software effort prediction: a hyper-heuristic decision-tree based approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Automatic design of decision-tree algorithms with evolutionary algorithms
Evolutionary Computation
Variable precision rough set based decision tree classifier
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Hybrid approaches for approximate reasoning
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It is important to use a better criterion in selection and discretization of attributes for the generation of decision trees to construct a better classifier in the area of pattern recognition in order to intelligently access huge amount of data efficiently. Two well-known criteria are gain and gain ratio, both based on the entropy of partitions. We propose in this paper a new criterion based also on entropy, and use both theoretical analysis and computer simulation to demonstrate that it works better than gain or gain ratio in a wide variety of situations. We use the usual entropy calculation where the base of the logarithm is not two but the number of successors to the node. Our theoretical analysis leads some specific situations in which the new criterion works always better than gain or gain ratio, and the simulation result may implicitly cover all the other situations not covered by the analysis.