Extracting rules by destructive learning

A method is presented for extracting general rules from a trained artificial neural network (ANN), which is trained by destructive learning. The method presented here takes advantage of the pruned network which contains more exact knowledge regarding the problem. The method consists of three phases: training, pruning, and rule-extracting. The training phase is concerned with ANN learning, using a general backpropagation (BP) algorithm. In the pruning phase, redundant hidden units and links are deleted trained network, and then, the link weights remaining in the network are re-trained to obtain near-saturated outputs from hidden units. The rule-extracting algorithm uses the pruned network to extract rules. After applying the proposed method to the MONK´s problems testbed, we found 6, 27, and 20 rules which could classify all 432 testing data with 100, 100, and 98.1% accuracy for each MONK´s problem, respectively. In addition, the proposed method outperformed most other machine learning methods with which it was compared