The application of certainty factors to neural computing for rule discovery

Discovery of domain principles has been a major long-term goal for scientists. The paper presents a system called DOMRUL for learning such principles in the form of rules. A distinctive feature of the system is the integration of the certainty factor (CF) model and a neural network. These two elements complement each other. The CF model offers the neural network better semantics and generalization advantage, and the neural network overcomes possible limitations such as inaccuracies and overcounting of evidence associated with certainty factors. It is a major contribution of the paper to show mathematically the quantizability nature of the CFNet since previously the quantizability of the CF model was demonstrated only empirically. The rule discovery system can be applied to any domain without restriction on both the rule number and rule size. In a hypothetical domain, DOMRUL discovered complex domain rules at a considerably higher accuracy than a commonly used rule-learning program C4.5 in both normal and noisy conditions. The scalability in a large domain is also shown. On a real data set concerning promoters prediction in molecular biology, DOMRUL learned rules with more complete semantics than C4.5.