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The Alternating Decision Tree Learning Algorithm

By Yoav Freund and Llew Mason

Abstract

The application of boosting procedures to decision tree algorithms has been shown to produce very accurate classifiers. These classifiers are in the form of a majority vote over a number of decision trees. Unfortunately, these classifiers are often large, complex and difficult to interpret. This paper describes a new type of classification rule, the alternating decision tree, which is a generalization of decision trees, voted decision trees and voted decision stumps. At the same time classifiers of this type are relatively easy to interpret

Publisher: Morgan Kaufmann
Year: 1999
OAI identifier: oai:CiteSeerX.psu:10.1.1.19.3673
Provided by: CiteSeerX
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