Galois Lattice: a framework for concept learning. Design, evaluation and refinement

Abstract

The previously-reported LEGAL system is an empirical machine learning system based on Galois Lattice. Its aim is first to produce a semi-lattice from a concept denoted by a set of objects which are described with binary attributes. Then using some selected attribute conjunctions in the semi-lattice and a majority vote principle, LEGAL predicts new examples from unseen objects. This paper describes a new version LEGAL-E and its application to two biological problems: the prediction of splice junctions sites and the promoter recognition. Results obtained are far better than those of some symbolic learning systems, and are as better as those of some best neural networks methods. Moreover some empirical properties shared by LEGAL-E and neural networks are described. Finally this paper shows how the semi-lattice can be used as a dynamic neural network architecture in order to combine both learning techniques for knowledge refinement.<>