Title :
Automated knowledge acquisition for diagnosis
Author :
Sestito, Sabrina ; Goss, Simon
Author_Institution :
Air Oper. Div., DSTO Melbourne, Ascot Vale, Vic., Australia
fDate :
29 Nov-2 Dec 1994
Abstract :
A distinction between machine learning and automated knowledge acquisition lies in the degree of involvement by experts, and the importance placed on criteria of comprehensibility, efficiency and performance. In this study, we apply three machine learning methods to the LED, engine diagnosis and head injury recovery times. We report comparative results of the performance in constructing classifier systems. A qualitative assessment of their utility for automating part of the knowledge acquisition process in constructing diagnostic knowledge based systems is offered
Keywords :
diagnostic expert systems; knowledge acquisition; knowledge based systems; learning (artificial intelligence); automated knowledge acquisition; classifier systems; diagnosis; diagnostic knowledge based systems; knowledge acquisition; machine learning; qualitative assessment; Brain injuries; Classification tree analysis; Decision trees; Engines; Knowledge acquisition; Knowledge based systems; Learning systems; Light emitting diodes; Machine learning; Testing;
Conference_Titel :
Intelligent Information Systems,1994. Proceedings of the 1994 Second Australian and New Zealand Conference on
Conference_Location :
Brisbane, Qld.
Print_ISBN :
0-7803-2404-8
DOI :
10.1109/ANZIIS.1994.397002
