Carla Rebelo, Carlos Soares, Joaquim Pinto da Costa
The problem of learning rankings is receiving increased attention from several research communities. In this paper we empirically evaluate an adaptation of the algorithm of learning decision trees for rankings. Our experiments are carried out on some metalearning problems, which consist of relating characteristics of learning problems to the relative performance of learning algorithms. We obtain positive results which, somewhat surprisingly, indicate that the method predicts more accurately the top ranks.
Subjects: 12. Machine Learning and Discovery; 15.6 Decision Trees
Submitted: May 16, 2008