Extended Space Decision Tree

Abstract

An extension of the attribute space of a dataset typically increases the prediction accuracy of a decision tree built for this dataset. Often attribute space is extended by randomly combining two or more attributes. In this paper, we propose a novel approach for the space extension where we only choose the combined attributes that have high classification capacity. We expect the inclusion of these attributes in the attribute space increases the prediction capacity of the trees built from the datasets with the extended space. We conduct experiments on five datasets coming from the UCI machine learning repository. Our experimental results indicate that the proposed space extension leads to the tree of higher accuracy than the case where original attribute space is used. Moreover, the experimental results demonstrate a clear superiority of the proposed technique over an existing space extension technique.