Active generation of training examples in meta-regression

Abstract

Meta-Learning predicts the performance of learning algorithms based on features of the learning problems. Meta-Learning acquires knowledge from a set of meta-examples, which store the experience obtained from applying the algorithms to problems in the past. A limitation of Meta-Learning is related to the generation of meta-examples. In order to construct a meta-example, it is necessary to empirically evaluate the algorithms on a given problem. Hence, the generation of a set of meta-examples may be costly depending on the context. In order to minimize this limitation, the use of Active Learning is proposed to reduce the number of required meta-examples. In this paper, we evaluate this proposal on a promising Meta-Learning approach, called Meta-Regression. Experiments were performed in a case study to predict the performance of learning algorithms for MLP networks. A significant performance gain was observed in the case study when Active Learning was used to support the generation of meta-examples. © 2009 Springer Berlin Heidelberg.