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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 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 tree, voted decision trees and voted stumps. At the same time classifiers of this type are relatively easy to interpret.
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