Active Selection of Training Examples for Meta-Learning

Abstract

Meta-learning has been used to relate the performance of algorithms and the features of the problems being tackled. The knowledge in meta-learning is acquired from a set of meta-examples which are generated from the empirical evaluation of the algorithms on problems in the past. In this work, active learning is used to reduce the number of meta-examples needed for meta-learning. The motivation is to select only the most relevant problems for meta-example generation, and consequently to reduce the number of empirical evaluations of the candidate algorithms. Experiments were performed in two different case studies, yielding promising results.