Selective generation of training examples in active meta-learning

Meta-Learning has been successfully applied to acquire knowledge used to support the selection of learning algorithms. Each training example in Meta-Learning (i.e. each meta-example) is related to a learning problem and stores the experience obtained in the empirical evaluation of a set of candidate algorithms when applied to the problem. The generation of a good set of meta-examples can be a costly process depending for instance on the number of available learning problems and the complexity of the candidate algorithms. In this work, we proposed the Active Meta-Learning, in which Active Learning techniques are used to reduce the set of meta-examples by selecting only the most relevant problems for meta-example generation. In an implemented prototype, we evaluated the use of two different Active Learning techniques applied in two different Meta-Learning tasks. The performed experiments revealed a significant gain in the Meta-Learning performance when the active techniques were used to support the meta-example generation.