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
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