C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Lazy Learning of Bayesian Rules
Machine Learning
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A decision tree-based attribute weighting filter for naive Bayes
Knowledge-Based Systems
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We propose a new model for supervised classification for data mining applications. This model is based on products of trees. The information given by each predictor variable is separately extracted by means of a recursive partition structure. This information is then combined across predictors using a weighted product model form, an extension of the naive Bayes model. Empirical results are presented comparing this new method with other methods in the machine learning literature, for several data sets. Two typical data mining applications, a chromosome identification problem and a forest cover type identification problem are used to illustrate the ideas. The new approach is fast and surprisingly accurate.