Title :
Eclectic rule extraction from Neural Networks using aggregated Decision Trees
Author_Institution :
Dept. of Comput. Sci., American Int. Univ.-Bangladesh (AIUB), Dhaka, Bangladesh
Abstract :
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.
Keywords :
decision trees; learning (artificial intelligence); neural nets; pattern recognition; HERETIC algorithm; aggregated decision tree; black box learner; complex nonlinear pattern learning; eclectic rule extraction; neural network training; pattern recognition algorithm; symbolic learning algorithm; tree combination; tree induction; Accuracy; Artificial neural networks; Biological neural networks; Decision trees; Neurons; Training; Decision Tree; Neural Network; Rule Extraction;
Conference_Titel :
Electrical & Computer Engineering (ICECE), 2012 7th International Conference on
Conference_Location :
Dhaka
Print_ISBN :
978-1-4673-1434-3
DOI :
10.1109/ICECE.2012.6471502