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Issue No.06 - November/December (1999 vol.11)
pp: 992-998
<p><b>Abstract</b>—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.</p>
Knowledge discovery, neural network, rule extraction, machine learning, certainty factor.
LiMin Fu, "Knowledge Discovery by Inductive Neural Networks", IEEE Transactions on Knowledge & Data Engineering, vol.11, no. 6, pp. 992-998, November/December 1999, doi:10.1109/69.824623
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