A neural network for learning domain rules with precision

To discover underlying domain regularities or rules has been a major long-term goal for scientific research (knowledge discovery) and engineering application (problem solving). However, when the domain rules get complex, current machine learning programs learn only approximate rather than true domain rules from a limited amount of observed data. This paper presents a new neural-network-based system which is intended for discovering precisely the domain rules with neither false positives nor false negatives. In a performance study, this system is ten times more accurate than the most well-known rule-learning system, C4.5, in terms of the rate of false rules induced from the data.