This paper proposes a new method for constructing binary classification trees. The aim is to build simple trees, i.e. trees which are as less complex as possible, thereby facilitating interpretation and favouring the balance between optimization and generalization in the test data sets. The proposed method is based on the metaheuristic strategy known as GRASP in conjunction with optimization tasks. Basically, this method modifies the criterion for selecting the attributes that determine the split in each node. In order to do so, a certain amount of randomisation is incorporated in a controlled way. We compare our method with the traditional method by means of a set of computational experiments. We conclude that the GRASP method (for small levels of randomness) significantly reduces tree complexity without decreasing classification accuracy.
['Joaquín A. Pacheco', 'Esteban Alfaro', 'Silvia Casado', 'Matías Gámez', 'Noelia García']
A GRASP method for building classification trees