Abstract
We demonstrate the use of an unsupervised learning technique called genetic algorithms to discover the association between a concept and its key attributes in concept characterization. The resulting concept-attribute associations are important domain concepts for knowledge engineers to structure interviews with the experts or to prepare representative data for inductive inference. Examples based on the part family identification problem in manufacturing are employed to illustrate the identification capability of our technique. Preliminary results from testing the technique in a SUN SPARC station 1+ indicate that it can be exploited as a decision support tool to assist knowledge engineers in the conceptualization stage of the knowledge acquisition process.
| Original language | English |
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| Pages | 160-173 |
| Number of pages | 14 |
| State | Published - 1996 |
| Event | 17th International Conference on Information Systems, ICIS 1996 - Cleveland, United States Duration: Dec 16 1996 → Dec 18 1996 |
Conference
| Conference | 17th International Conference on Information Systems, ICIS 1996 |
|---|---|
| Country/Territory | United States |
| City | Cleveland |
| Period | 12/16/96 → 12/18/96 |
Bibliographical note
Publisher Copyright:© 1996, Association for Information Systems. All rights reserved.
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
- Information Systems