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Weighted MCRDR: Deriving Information about Relationships between Classifications in MCRDR.

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Dazeley, R and Kang, BH (2003) Weighted MCRDR: Deriving Information about Relationships between Classifications in MCRDR. In: The 16th Australian Joint Conference on Artificial Intelligence (AI'03), 3-5 December, 2003, Perth, Australia.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Keywords: MCRDR, knowledge representation, Knowledge Based Systems, Document Rating, Case Based Reasoning, Neural Network, Multiple Classification.
Publisher: Springer
Page Range: pp. 245-255
Date Deposited: 04 Sep 2004
Last Modified: 18 Nov 2014 03:10
URI: http://eprints.utas.edu.au/id/eprint/57
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