WM_Photo_Ready.pdf (202.74 kB)
Weighted MCRDR: Deriving Information about Relationships\ between Classifications in MCRDR.
conference contribution
posted on 2023-05-26, 10:07 authored by Dazeley, R, Kang, BHMultiple 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
2903Pagination
245-255Publisher
SpringerPublication status
- Published
Event title
The 16th Australian Joint Conference on Artificial Intelligence (AI'03)Event Venue
Perth, AustraliaDate of Event (Start Date)
2003-12-03Date of Event (End Date)
2003-12-05Repository Status
- Open
Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC