Abstract
Classification trees are top-down induction of labeled sampling units into recursive order to get end nodes. Each end node representing those labeled units, which are in majority, otherwise considered as misclassified. In the top-down induction process, an evaluation criterion plays an important role to send maximum of the units having same label to the same node. To achieve this goal a "goodness of split" measure is calculated by using evaluation criteria e. g. Gini function, Twoing rule, Entropy function etc. for each distinct value of each variable and finally chooses one, which enhances the purity. Almost all the evaluation criteria provide the same results in terms of misclassified units, deviance and number of end nodes. Here we obtain simulation results on the proposed evaluation criteria (Azam, et al., 2007) to split a node, which fulfil all the required properties of any evaluation criteria (Breiman et al., 1984) and also propose a top-down sequential pruning scheme.
We conducted a simulation study to test the performance of the proposed evaluation criterion using the top-down sequential pruning scheme over many real life datasets available under UCI Machine Learning Repository and observed that the proposed strategy provides improved results.