Speeding up logistic model tree induction
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This is an author’s accepted version of a conference paper published in the Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. © 2005 Springer.
Abstract
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.
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Sumner, M., Frank, E. & Hall, M.A. (2005). Speeding up logistic model tree induction. In A. Jorge et al.(Eds.), Proceedings of 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005(pp. 675-683). Berlin, Germany: Springer
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Springer-Verlag Berlin Heidelberg