Abstract

. The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications. Keywords: decision tree, incremental induction, direct metric, binary test, example incorporation, missing value, tree transposition, installed test, virtual pruning, update cost. 1. Introduction Decision tree induction offers a highly practical method for generalizing from examples whose class membership is known. The most common approach to inducing a decision tree is to partition the labelled examples recursively until a stopping criterion is met. The partition is defined by selectin...

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