Rated MCRDR: Finding non-Linear Relationships Between Classifications in MCRDR.

摘要

Multiple Classification Ripple Down Rules (MCRDR) is a simple and effective knowledge acquisition technique that produces representations, or knowledge maps, of a human experts' knowledge of a particular domain. This knowledge map can then be used to automate and help the user perform classification and categorisation of cases while still being able to add more refined knowledge incrementally. While MCRDR has been applied in many domains, work on understanding the meta-knowledge acquired or using the knowledge to derive new information is still in its infancy. This paper will introduce a technique called Rated MCRDR (RM), which looks at deriving and learning information about both linear and non-linear relationships between the multiple classifications within MCRDR. This method uses the knowledge received in the MCRDR knowledge map to derive additional information that allows improvements in functionality within existing domains, to which MCRDR is currently applied, as well as opening up the possibility of new problem domains. Preliminary testing shows that there exists a strong potential for RM to quickly and effectively learn meaningful ratings.