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Weighted MCRDR: Deriving information about relationships between classifications in MCRDR

conference contribution
posted on 2003-01-01, 00:00 authored by Richard DazeleyRichard Dazeley, B H Kang
Multiple Classification Ripple Down Rules (MCRDR) is a knowledge acquisition technique that produces representations, or knowledge maps, of a human expert's knowledge of a particular domain. However, work on gaining an understanding of the knowledge acquired at a deeper meta-level or using the knowledge to derive new information is still in its infancy. This paper will introduce a technique called Weighted MCRDR (WM), which looks at deriving and learning information about the relationships between multiple classifications within MCRDR by calculating a meaningful rating for the task at hand. This is not intended to reduce the knowledge acquisition effort for the expert. Rather, it is attempting to use the knowledge received in the MCRDR knowledge map to derive additional information that can allow improvements in functionality of MCRDR in many problem domains. Preliminary testing shows that there exists a strong potential for WM to quickly and effectively learn meaningful weightings.

History

Volume

2903

Pagination

245 - 255

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783540206460

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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