ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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
Competence-Guided Case-Base Editing Techniques
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Learning to Adapt for Case-Based Design
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Case Representation Issues for Case-Based Reasoning from Ensemble Research
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
When Two Case Bases Are Better than One: Exploiting Multiple Case Bases
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Distributed case-based reasoning
The Knowledge Engineering Review
k-NN Aggregation with a Stacked Email Representation
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Identifying and eliminating mislabeled training instances
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Decision diagrams: fast and flexible support for case retrieval and recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Adaptive case-based reasoning using retention and forgetting strategies
Knowledge-Based Systems
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
On dataset complexity for case base maintenance
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
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Case-base administrators face a choice of many maintenance algorithms. It is well-known that these algorithms have different biases that cause them to perform inconsistently over different datasets. In this paper, we demonstrate some of the biases of the most commonly-used maintenance algorithms. This motivates our new approach: maintenance by a committee of experts (MACE). We create composite algorithms that comprise more than one individual maintenance algorithm in the hope that the strengths of one algorithm will compensate for the weaknesses of another. In MACE, we combine algorithms in two ways: either we put them in sequence so that one runs after the other, or we allow them to run separately and then vote as to whether a case should be deleted or not. We define a grammar that describes how these composites are created. We perform experiments based on 27 diverse datasets. Our results show that the MACE approach allows us to define algorithms with different trade-offs between accuracy and the amount of deletion.