Instance-Based Learning Algorithms
Machine Learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
Complexity-guided case discovery for case based reasoning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A knowledge-light approach to regression using case-based reasoning
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Complexity profiling for informed case-base editing
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Modified blame-based noise reduction for concept drift
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Concept drift detection via competence models
Artificial Intelligence
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Knowledge maintenance for Case-Based Reasoning systems is an important knowledge engineering task despite the availability of initial case knowledge and new cases to extend it. For classification systems it is essential that different scenarios for the various classes are well represented and decision boundaries are well defined in the case knowledge. A complexity-based competence metric is proposed that identifies redundant and error-causing cases to be deleted. The metric informs a maintenance tool that enables the engineer to experiment and balance conflicting objectives. Complexity-informed maintenance outperforms benchmark algorithms for redundancy and error reduction tasks.