The role of decision tree representation in regression problems - An evolutionary perspective

A regression tree is a type of decision tree that can be applied to solve regression problems. One of its characteristics is that it may have at least four different node representations; internal nodes can be associated with univariate or oblique tests, whereas the leaves can be linked with simple constant predictions or multivariate regression models. The objective of this paper is to demonstrate the impact of particular representations on the induced decision trees. As it is difficult if not impossible to choose the best representation for a particular problem in advance, the issue is investigated using a new evolutionary volutionary algorithms ata mining egression trees elf-adaptable representation algorithm for the decision tree induction with a structure that can self-adapt to the currently analyzed data. The proposed solution allows different leaves and internal nodes representation within a single tree. Experiments performed using artificial and real-life datasets show the importance of tree representation in terms of error minimization and tree size. In addition, the presented solution managed to outperform popular tree inducers with defined homogeneous representations. © 2016 Elsevier B.V. All rights reserved.