CLANN: Concept Lattice-based Artificial Neural Network for Supervised Classification.

Résumé

Abstract. Multi-layer neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. As they often produce incomprehensible models they are not widely used in data mining applications. To avoid such limitations, comprehensive models have been previously introduced making use of an apriori knowledge to build the network architecture. They permit to neural network methods to deserve a place in the tool boxes of data mining specialists. However, as the apriori knowledge is not always available for every new dataset, we hereby propose a novel approach that generates a concept semi-lattice from initial dataset, to directly build the neural network architecture. Carried out experiments showed the soundness and efficiency of our approach on various UCI. 1