C4.5: programs for machine learning
C4.5: programs for machine learning
An Information-Theoretic Approach to the Pre-pruning of Classification Rules
Proceedings of the IFIP 17th World Computer Congress - TC12 Stream on Intelligent Information Processing
Active Learning Using a Constructive Neural Network Algorithm
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
3DM: Domain-oriented Data-driven Data Mining
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
3DM: Domain-oriented Data-driven Data Mining
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
On achieving semi-supervised pattern recognition by utilizing tree-based SOMs
Pattern Recognition
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The automatic induction of classification rules from examples in the form of a classification tree is an important technique used in data mining. One of the problems encountered is the overfitting of rules to training data. In some cases this can lead to an excessively large number of rules, many of which have very little predictive value for unseen data. This paper describes a means of reducing overfitting known as J-pruning, based on the J-measure, an information theoretic means of quantifying the information content of a rule. It is demonstrated that using J-pruning generally leads to a substantial reduction in the number of rules generated and an increase in predictive accuracy. The advantage gained becomes more pronounced as the proportion of noise increases.