A Meta-Classification Framework

In this paper we propose a meta-classification framework which is able to represent the accumulated experience from a base-learner in form of knowledge-base and to exploit it whenever a new task has to be tackled. Our meta-dassificr represents a particular meta-learning strategy where a single learning algorithm is employed both at base- and meta-level of learning. The proposed meta-classification framework is based on a neuro-fuzzy approach where connectionist paradigm is integrated with fuzzy logic for the management of the accumulated knowledge during the learning process. A complete experimental session is presented, in order to show the suitability of the system. Additionally, in a final experimental evaluation, we show that the obtained accuracy results are comparable with the ones obtained by another meta classification scheme presented in literature.