Greedy rule generation from discrete data and its use in neural network rule extraction

This paper proposes GRG (greedy rule generation) algorithm for generating classification rules from a data set with discrete attributes. The algorithm is "greedy" in the sense that at every iteration, it searches for the best rule to generate. The criteria for the best rule include the number of samples that it covers, the number of attributes involved in the rule, and the size of the input subspace it covers. This method is applied for extracting rules from neural networks that have been trained and pruned for solving classification problems. Neural networks with one hidden layer are trained and the proposed GRG algorithm is applied to their discretized hidden unit activation values. Our results show that rule extraction with the GRG method produces rule sets that are more accurate and concise compared to those obtained by a decision tree method and an existing neural network rule extraction method.