Computational Intelligence for Meta-Learning: A Promising Avenue of Research

Abstract

Abstract. The common practices of machine learning appear to be frustrated by a number of theoretical results denying the possibility of any meaningful implementation of a "superior" learning algorithm. However, there exist some general assumptions that, even when overlooked, preside the activity of researchers and practitioners. A thorough reflection over such essential premises brings forward the meta-learning approach as the most suitable for escaping the long-dated riddle of induction claiming also an epistemologic soundness. Several examples of meta-learning models can be found in literature, yet the combination of computational intelligence techniques with meta-learning models still remains scarcely explored. Our contribution to this particular research line consists in the realisation of Mindful, a meta-learning system based on the neurofuzzy hybridisation. We present the Mindful system firstly situating it inside the general context of the meta-learning frameworks proposed in literature. Finally, a complete session of experiments is illustrated, comprising both base-level and meta-level learning activity. The appreciable experimental results underline the suitability of the Mindful system for managing past accumulated learning experience while facing novel tasks.