Abstract:
Typically, decision tree construction algorithms apply a single "goodness of split " criterion to form each test node of the tree. It is a hypothesis of this research that better results can be obtained if during tree construction one applies a split criterion suited to the "location " of the test node in the tree. Specifically, given the objective of maximizing predictive accuracy, test nodes near the root of the tree should be chosen using a measure based on information theory, whereas test nodes closer to the leaves of the pruned tree should be chosen to maximize classification accuracy on the training set. The results of an empirical evaluation illustrate that adapting the split criterion to node location can improve classification performance.
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