Transfer Learning for Synthetic Examples Selection in Meta-learning

In Meta-learning, training examples are generated from experiments performed with a pool of candidate algorithms in a number of problems (real or synthetic). Generating a good set of examples can be difficult due to the low availability of real datasets in some domains and the high computational cost of labeling. In this paper, we focus on the selection of training meta-examples by combining data manipulation and Transfer Learning via One-class classification. So, the most relevant examples are selected to be labeled. Our experiments revealed that it is possible to reduce the computational cost of generating meta- examples and maintain the meta-learning performance.