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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Speeding Up Logistic Model Tree Induction
verfasst von : Marc Sumner, Eibe Frank, Mark Hall
Erschienen in: Knowledge Discovery in Databases: PKDD 2005
Verlag: Springer Berlin Heidelberg
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Logistic Model Trees have been shown to be very accurate and compact classifiers [8]. Their greatest disadvantage is the computational complexity of inducing the logistic regression models in the tree. We address this issue by using the AIC criterion [1] instead of cross-validation to prevent overfitting these models. In addition, a weight trimming heuristic is used which produces a significant speedup. We compare the training time and accuracy of the new induction process with the original one on various datasets and show that the training time often decreases while the classification accuracy diminishes only slightly.