Abstract
A new classification tree algorithm is presented. It has a novel variable selection algorithm that can effectively detect interactions. The algorithm uses a look-ahead approach that considers not only the significance at the current node, but also the significance at child nodes to detect the interaction. It is also different from other classification tree methods in that it finds the splitting point using the odds ratio. To evaluate the predictive performance of the newly proposed tree algorithm, an empirical study of 27 real or artificial data sets is performed. As a result of the experiment, the proposed algorithm shows at least similar or significantly better performance than the well-known and successful decision tree methods: Ctree, CART and CRUISE.
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