Decision Trees with at Most 19 Vertices for Knowledge Representation

We study decision trees as a means of representation of knowledge. To this end, we design two techniques for the creation of CART (Classification and Regression Tree)-like decision trees that are based on bi-objective optimization algorithms. We investigate three parameters of the decision trees constructed by these techniques: number of vertices, global misclassification rate, and local misclassification rate.