Towards a generalization of decompositional approach of rule extraction from multilayer artificial neural network
Norbert Tsopzé, Engelbert Mephu Nguifo, Gilbert Tindo · 2011
The current development of knowledge discovery domain has pointed out a high number of applications where the need of explanation is at the heart of the process. Using neural networks for those applications requires to be able to provide a set of rules extracted from the trained neural networks, that can help the user to comprehend the learning process. The current literature reports two kinds of rules: `if condition then conclusion' (called if-then) and `if m of conditions then conclusion' (also called MofN). We propose a new method able to extract one intermediate structure (called generators list) from which it is possible to extract both forms of rules. The extracted structure is a generic representation that gives the possibility to the user to visualize each form of rules extracted from the multilayer artificial neural networks.