Article ID Journal Published Year Pages File Type
396026 Information Sciences 2007 21 Pages PDF
Abstract

This paper examines a decision-tree framework for instance-space decomposition. According to the framework, the original instance-space is hierarchically partitioned into multiple subspaces and a distinct classifier is assigned to each subspace. Subsequently, an unlabeled, previously-unseen instance is classified by employing the classifier that was assigned to the subspace to which the instance belongs. After describing the framework, the paper suggests a novel splitting-rule for the framework and presents an experimental study, which was conducted, to compare various implementations of the framework. The study indicates that using the novel splitting-rule, previously presented implementations of the framework, can be improved in terms of accuracy and computation time.

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Physical Sciences and Engineering Computer Science Artificial Intelligence
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