Decision-tree instance-space decomposition with grouped gain-ratio

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.