A top-down construction of class decision trees with selected features and classifiers

Tree-type expression of multi-class problems is known to be useful for drawing several insights from a given problem and for improving the performance of classifiers. The authors have already proposed a bottom-up procedure to construct such a tree, called a “class decision tree”, but a top-down procedure is still worth studying. In this paper, we propose a simple top-down procedure, and compare those two procedures. In addition, we discuss the effectiveness of classifier selection and feature selection applied to every node in class decision trees, and the preferable order of them as well.