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PKDD
2005
Springer

Speeding Up Logistic Model Tree Induction

13 years 9 months ago
Speeding Up Logistic Model Tree Induction
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 crossvalidation 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.
Marc Sumner, Eibe Frank, Mark A. Hall
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where PKDD
Authors Marc Sumner, Eibe Frank, Mark A. Hall
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