Decision tree grafting was first conceived as a way of demonstrating that of two models that perform identically on training data, the simplest is not most likely to have the lowest accuracy.
However, it has also proven to be a practical learning algorithm. Indeed, its Weka implementation has been used in a number of scientific applications, including the following.
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