MINDFUL: a framework for meta-inductive neuro-fuzzy learning

Common inductive learning strategies offer tools for knowledge acquisition, but possess some inherent limitations due to the use of fixed bias during the learning process. To overcome the limitations of such base-learning approaches, a research trend explores the potentialities of meta-learning, which is oriented to the development of mechanisms based on a dynamical search of bias. This may lead to an improvement of the base-learner performance on specific learning tasks, by profiting of the accumulated past experience. In this paper, we present a meta-learning framework called MINDFUL (Meta INDuctive neuro-FUzzy Learning) which is founded on the integration of connectionist paradigms and fuzzy knowledge management. Due to its peculiar organisation, MINDFUL can be exploited on different levels of application, being able to accumulate learning experience in cross-task contexts. This specific knowledge is gathered during the meta-learning activity and it is exploited to suggest parametrisation for future base-learning tasks. The evaluation of the MINDFUL system is detailed through an ensemble of experimental sessions involving both synthetic domains and real-world data.