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Eclectic rule extraction from Neural Networks using aggregated Decision Trees

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1 Author(s)
Iqbal, M.R.A. ; Dept. of Comput. Sci., American Int. Univ.-Bangladesh (AIUB), Dhaka, Bangladesh

Neural Network is a powerful pattern recognition algorithm capable of learning complex non-linear patterns. However, Neural Networks have a well-known drawback of being a “Black Box” learner that is not comprehensible or transferable thus making it unsuitable tasks that require a rational justification for making a decision. Rule Extraction methods can resolve this limitation by extracting comprehensible rules from a trained Network. In this paper, we present an algorithm called HERETIC that uses a symbolic learning algorithm (Decision Tree) on each unit of the Neural Network. Experiments and theoretical analysis show HERETIC generates highly accurate rules that closely approximates the Neural Network.

Published in:

Electrical & Computer Engineering (ICECE), 2012 7th International Conference on

Date of Conference:

20-22 Dec. 2012