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AUSAI
2003
Springer

Weighted MCRDR: Deriving Information about Relationships between Classifications in MCRDR

9 years 9 months ago
Weighted MCRDR: Deriving Information about Relationships between Classifications in MCRDR
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.
Richard Dazeley, Byeong Ho Kang
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where AUSAI
Authors Richard Dazeley, Byeong Ho Kang
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