Title :
A neural network for learning domain rules with precision
Author_Institution :
Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL, USA
Abstract :
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
Keywords :
learning (artificial intelligence); learning systems; neural nets; problem solving; CFNet; domain rules; neural network; problem solving; rule-learning; Data mining; Engines; Feedforward neural networks; Knowledge engineering; Learning systems; Machine learning; Machine learning algorithms; Multi-layer neural network; Neural networks; Problem-solving;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831136
