Learning in certainty-factor-based multilayer neural networks for classification

The computational framework of rule-based neural networks inherits from the neural network and the inference engine of an expert system. In one approach, the network activation function is based on the certainty factor (CF) model of MYCIN-like systems. In this paper, it is shown theoretically that the neural network using the CF-based activation function requires relatively small sample sizes for correct generalization. This result is also confirmed by empirical studies in several independent domains.