Logistic Model Tree With Modified AIC
نویسندگان
چکیده
Logistic Model Trees have been shown to be very accurate and compact classifiers. Their greatest disadvantage is the computational complexity of inducing the logistic regression models in the tree. This issue is addressed by using the modified AIC criterion instead of crossvalidation to prevent overfitting these models. In addition, to fill the missing values, mean and mode are used class wise for numeric and nominal attributes respectively. The comparison of training time and accuracy of the new induction process with the original one on various datasets and show that the training time often decreases and the classification accuracy also increase slightly.
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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 ...
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