Rule Extraction from Trained ANN: A Survey
Ashish Darbari · ePrints Soton (University of Southampton) · 2000
A survey of several well known rule extraction techniques is presented in my report, in the light of a broader paradigm of connectionist-symbolic learning. In the first part of the report I have covered some introductory aspects about machine learning, and investigated the reasons for a possible connectionist-symbolic integration, thereby presenting a hybrid learning framework. Within this hybrid learning framework, my report focuses on the survey of Rule extraction techniques from Trained Artificial Neural Networks (ANNs). I have presented the techniques, and thereby compare them on several grounds, and investigate the relative merits and demerits of each one of them. Towards the end, there...