A neural network for learning domain rules with precision

Author

Fu, LiMin

Author_Institution

Dept. of Comput. & Inf. Sci., Florida Univ., Gainesville, FL, USA

Volume

2

fYear

1999

fDate

36342

Firstpage

1229

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.831136

Filename

831136