Rule extraction from local cluster neural nets

This paper describes RULEX, a technique for providing an explanation component for local cluster (LC) neural networks. RULEX extracts symbolic rules from the weights of a trained LC net. LC nets are a special class of multilayer perceptrons that use sigmoid functions to generate localised functions. LC nets are well suited to both function approximation and discrete classification tasks. The restricted LC net is constrained in such a way that the local functions are 'axis parallel' thus facilitating rule extraction. This paper presents results for the LC net on a wide variety of benchmark problems and shows that RULEX produces comprehensible, accurate rules that exhibit a high degree of fidelity with the LC network from which they were extracted. (C) 2002 Elsevier Science B.V. All rights reserved.