Linear Machine Decision Trees
Paul E. Utgoff, Carla E. Brodley · 1991
This article presents an algorithm for inducing multiclass decision trees with multivariate tests at internal decision nodes. Each test is constructed by training a linear machine and eliminating variables in a controlled manner. Empirical results demonstrate that the algorithm builds small accurate trees across a variety of tasks. 1 Introduction One of the fundamental research problems in machine learning is how to learn from examples. From a sequence or set of training examples, each labeled with its correct class name, a machine learns by forming or selecting a generalization of the training examples. This process, also known as supervised learning, is useful for real classification tasks, e.g. disease diagnosis, and for problem solving tasks in which control decisions depend on classification, e.g. rule applicability. The ability to generalize is fundamental to intelligence because it allows one to reason in accordance with predictions that are often correct. This article focuse...