C4.5: programs for machine learning
C4.5: programs for machine learning
A New Criterion in Selection and Discretization of Attributes for the Generation of Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-Driven Statistical Discretization of Continuous Attributes (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Minimum splits based discretization for continuous features
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Relative Unsupervised Discretization for Association Rule Mining
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Generating Linear Regression Rules from Neural Networks Using Local Least Squares Approximation
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
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The paper describes a new, context-sensitive discretization algorithm that combines aspects of unsupervised (class-blind) and supervised methods. The algorithm is applicable to a wide range of machine learning and data mining problems where continuous attributes need to be discretized. In this paper, we evaluate its utility in a regression-by-classification setting. Preliminary experimental results indicate that the decision trees induced using this discretization strategy are significantly smaller and thus more comprehensible than those learned with standard discretization methods, while losing only minimally in numerical prediction accuracy. This may be a considerable advantage in machine learning and data mining applications where comprehensibility is an issue.