Logistic Model Tree With Modified AIC

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.