This paper introduces a novel evolutionary approach which can be applied to supervised, semi-supervised and unsupervised learning tasks. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods, and integrates these into a Grammatical Evolution framework. With minor adaptations to the objective function the system can be trivially modified to work with the conceptually different paradigms of supervised, semi-supervised and unsupervised learning. The framework generates human readable solutions which explain the mechanics behind the classification decisions, offering a significant advantage over existing paradigms for unsupervised and semi-supervised learning. GEML is studied on a range of multi-class classification problems and is shown to be competitive with several state of the art multi-class classification algorithms.
['Jeannie Fitzgerald', 'R. Muhammad Atif Azad', 'Conor Ryan']
GEML: Evolutionary unsupervised and semi-supervised learning of multi-class classification with Grammatical Evolution