Topological sorting of large networks
Communications of the ACM
Knowledge Acquisition without Analysis
Proceedings of the 7th European Workshop on Knowledge Acquisition for Knowledge-Based Systems
Generalising Ripple-Down Rules (Short Paper)
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
NRDR for the Acquisition of Search Knowledge
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
A Maintenance Approach to Case-Based Reasoning
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Detection of CAN by ensemble classifiers based on ripple down rules
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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A new Ripple Down Rules based methodology which allows for the creation of rules that use classifications as conditions has been developed, and is entitled Multiple Classification Ripple Round Rules (MCRRR). Since it is difficult to recruit human experts in domains which are appropriate for testing this kind of method, simulated evaluation has been employed. This paper presents a simulated evaluation approach for assessing two separate aspects of the MCRRR method, which have been identified as potential areas of weakness. Namely,"Is the method useful in practice?" and"Is the method acceptable, computationally?" It was found that the method appears to be of value in some, but not many,"traditional" multi-class domains, and that due to computational concerns with one aspect of the method it is considered unsuitable for domains with a very large number of cases or rules. These issues are discussed and solutions are proposed.