Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
On the Consistency of Information Filters for Lazy Learning Algorithms
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Using Decision Tree Induction for Discovering Holes in Data
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Discovering interesting holes in data
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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
Index driven selective sampling for CBR
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Black Swans, Gray Cygnets and Other Rare Birds
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
An Active Approach to Automatic Case Generation
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Informed case base maintenance: a complexity profiling approach
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Catching Gray Cygnets: an initial exploration
Proceedings of the 13th International Conference on Artificial Intelligence and Law
Complexity profiling for informed case-base editing
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
How many cases do you need? assessing and predicting case-base coverage
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
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The distribution of cases in the case base is critical to the performance of a Case Based Reasoning system. The case author is given little support in the positioning of new cases during the development stage of a case base. In this paper we argue that classification boundaries represent important regions of the problem space. They are used to identify locations where new cases should be acquired. We introduce two complexity-guided algorithms which use a local complexity measure and boundary identification techniques to actively discover cases close to boundaries. The ability of these algorithms to discover new cases that significantly improve the accuracy of case bases is demonstrated on five public domain classification datasets.