Scientific publications
* (Corresponding author)
Other affiliations: Unité de Modélisation Mathématique et Informatique des Systèmes Complexes
Other affiliations: Unité de Modélisation Mathématique et Informatique des Systèmes Complexes
Cezar Mbiethieu
Other affiliations: Unité de Modélisation Mathématique et Informatique des Systèmes Complexes
Laure Pauline Fotso
Other affiliations: Unité de Modélisation Mathématique et Informatique des Systèmes Complexes
Engelbert Mephu Nguifo
Other affiliations: Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes · University of Clermont Auvergne
International Journal of General Systems
We propose a novel approach to define Artificial Neural Network(ANN) architecture from Boolean factors. ANNs are a subfield of machine learning applicable to several areas of life. However, defining its architecture for solving a given problem is not formalized and remains an open research problem. Since it is difficult to look into the network and figure out exactly what it has learnt, the complexity of such a technique makes its interpretation more tedious. We propose in this paper to build feedforward ANNs using the optimal factors obtained from the Boolean context representing a data. Since optimal factors completely cover the data and therefore give an explanation to these data, We could give an interpretation to the neurons activation and justify the presence of a neuron in our proposed neural network. We show through experiments and comparisons on the use data sets that this approach provides relatively better results for some key performance measures.
