Automated knowledge acquisition for diagnosis

Author

Sestito, Sabrina ; Goss, Simon

Author_Institution

Air Oper. Div., DSTO Melbourne, Ascot Vale, Vic., Australia

fYear

1994

fDate

29 Nov-2 Dec 1994

Firstpage

427

Lastpage

431

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ANZIIS.1994.397002

Filename

397002