Instance-Based Learning Algorithms
Machine Learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
An Interactive Visualisation Tool for Case-Based Reasoners
Applied Intelligence
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
The Utility Problem Analysed: A Case-Based Reasoning Perspective
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Modelling the Competence of Case-Bases
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Competence-Guided Case-Base Editing Techniques
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Building Compact Competent Case-Bases
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Case-Based Reasoning Technology, From Foundations to Applications
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
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Evaluation Measures for TCBR Systems
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Robust Measures of Complexity in TCBR
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
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
On dataset complexity for case base maintenance
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Concept drift detection via competence models
Artificial Intelligence
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The contents of the case knowledge container is critical to the performance of case-based classification systems. However the knowledge engineer is given little support in the selection of suitable techniques to maintain and monitor the case-base. In this paper we present a novel technique that provides an insight into the structure of a case-base by means of a complexity profile that can assist maintenance decision-making and provide a benchmark to assess future changes to the case-base. We also introduce a complexity-guided redundancy reduction algorithm which uses a local complexity measure to actively retain cases close to boundaries. The algorithm offers control over the balance between maintaining competence and reducing case-base size. The ability of the algorithm to maintain accuracy in a compacted case-base is demonstrated on seven public domain classification datasets.