Active Meta-Learning with Uncertainty Sampling and Outlier Detection

Meta-Learning has been used to predict the performance of learning algorithms based on descriptive features of the learning problems. Each training example in this context, i.e. each meta-example, stores the features of a given problem and information about the empirical performance obtained by the candidate algorithms on that problem. The process of constructing a set of meta-examples may be expensive, since for each problem avaliable for meta-example generation, it is necessary to perform an empirical evaluation of the candidate algorithms. Active Meta-Learning has been proposed to overcome this limitation by selecting only the most informative problems in the meta-example generation. In this work, we proposed an Active Meta-Learning method which combines Uncertainty Sampling and Outlier Detection techniques. Experiments were performed in a case study, yielding significant improvement in the Meta-Learning performance.