Multiple-classification ripple-down rules (MCRDR) is an incremental
knowledge acquisition technique which preserves the benefits and essential strategy of
ripple-down rules (RDR) in handling the multiple classifications. MCRDR, the extension of RDR, is based on the assumption that the knowledge an expert provides is essentially a justification for a conclusion in a particular context.
Implementations
Below is a list of implementations of MCRDR
* The alpha version of RDR(MCRDR) Framework was developed by UNSW and UTAS Research Team and funded by ARC (System available a
BESTRDR
* RDR(MCRDR) document classifier was developed by Dr.Yang Sok Kim and AProf.Byeong Ho Kang (System available a
BESTRDR
* RDR(MCRDR) smart expert system was developed by UTAS Research Team and funded by Hyundai Steel.
* Pacific Knowledge Systems (PKS) uses a commercial product called RippleDown Expert that is based on Multiple Classification Ripple Down Rules
Medscope Medication Review Mentoruses Multiple Classification Ripple Down Rules to identify adverse drug interactions with a patient's medicines regimen
BEST RDR Warehouse
BEST-RDR (Best Expert System Technique – Ripple Down Rule) website is freely accessible RDR publication and system warehouse that helps you to find programs and publications about RDR. A great amount of publications and programs based on RDR (MCRDR) are available to public.
What functions are available in the BEST RDR?
# BEST-RDR website provides detailed explanation of what the RDR and MCRDR are
# BEST-RDR provides every RDR(MCRDR) publication details from 1987 to 2013.
# BEST-RDR provides sources of various RDR(MCRDR) based system for download
See also
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Ripple-down rules
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Case-based reasoning
In artificial intelligence and philosophy, case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems.
In everyday life, an auto mechanic who fixes an engine by recalli ...
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Decision trees
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains condit ...
References
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Knowledge management
Rules
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