Controlling Complexity and Accuracy of Classification Decision Tree Extracted from Trained Artificial Neural Network

There is growing number of publications devoted to knowledge extraction from fully connected feed-forward artificial neural networks. Although there are not many publications covering ways allowing to control extracted knowledge complexity and precision. The higher complexity is, the higher accuracy can be gained. But in case ANN should be validated by domain expert or just be explainable it should be simple enough - this will lower accuracy of extracted knowledge. The current paper explores influence of parameters used for ANN pruning and neurons outputs discretization and clustering onto accuracy of extracted classification decision tree. Hence reader is presented with experimental validation of effects produced by variation in parameters combination.