Knowledge discovery by inductive neural networks
LiMin Fu · IEEE Transactions on Knowledge and Data Engineering · 1999
A new neural network model for inducing symbolic knowledge from empirical data is presented. This model capitalizes on the fact that the certainty factor-based activation function can improve the network generalization performance from a limited amount of training data. The formal properties of the procedure for extracting symbolic knowledge from such a trained neural network are investigated. In the domain of molecular genetics, a case study demonstrated that the described learning system effectively discovered the prior domain knowledge with some degree of refinement. Also, in cross-validation experiments, the system outperformed C4.5, a commonly used rule learning system.