Logical rule extraction from data by maximum neural networks

In this paper, a new neural computing method to extract logical rules from the training data sets is proposed. Maximum neural networks are used to train the weight and the threshold of the multilayered (feedforward) neural network (MLNN). The threshold and the weights of the MLNN are trained to be a logical function (AND/OR) with the multiple input. The maximum neural network constructs the logical function on the MLNN so that it is not necessary to extract rules from the trained MLNN. The proposed method was experimented for the classification problem, Monk´s problem 1. Experimental results showed that the proposed method learned the correct rule in more than 40% success rate