The truth is in there: current issues in extracting rules from trained feedforward artificial neural networks

A recognized impediment to the more widespread utilization of artificial neural networks (ANNs) is the absence of a capability to explain, in a human-comprehensible form, either the process by which a trained ANN arrives at a specific decision/result or, in general, the totality of knowledge embedded therein. There has been a proliferation of techniques aimed at redressing this situation and, in particular, for extracting the knowledge embedded in trained feedforward ANNs as sets of symbolic rules. However, if the dissemination of ideas in the field of ANN rule extraction is to proceed in a systematic manner, then it is essential that a rigorous taxonomy exists for categorizing the plethora of techniques being developed. This paper shows how one of the proposed schemas for categorizing ANN rule extraction techniques is able to accommodate such developments in the field. In addition attention is drawn to what are seen to be some of the key challenges in the area including the identification of factors which appear to limit what is actually achievable through the rule extraction process