Interpretation of Trained Neural Networks by Rule Extraction

Authors:
Vasile Palade;Ciprian-Daniel Neagu;Ron J. Patton
Affiliations:
-;-;-
Venue:
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Year:
2001

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Abstract

The paper focuses on the problem of rule extraction from neural networks, with the aim of transforming the knowledge captured in a trained neural network into a familiar form for human user. The ultimate purpose for us is to develop human friendly shells for neural network based systems. In the first part of the paper it is presented an approach on extracting traditional crisp rules out of the neural networks, while the last part of the paper presents how to transform the neural network into a set of fuzzy rules using an interactive fuzzy operator. The rules are extracted from ordinary neural networks, which have not a structure that facilitate the rule extraction. The neural network trained with the well known Iris data set was considered as benchmark problem.