Ensemble Case-Based Reasoning: Collaboration Policies for Multiagent Cooperative CBR
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Learning to Improve Case Adaption by Introspective Reasoning and CBR
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A regression based adaptation strategy for case-based reasoning
Eighteenth national conference on Artificial intelligence
Machine learning for information extraction in informal domains
Machine learning for information extraction in informal domains
A taxonomy of scientific workflow systems for grid computing
ACM SIGMOD Record
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
Combining Information Extraction Systems Using Voting and Stacked Generalization
The Journal of Machine Learning Research
Learning adaptation knowledge to improve case-based reasoning
Artificial Intelligence
Multi-case-base reasoning
Towards Case-Based Support for e-Science Workflow Generation by Mining Provenance
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
myExperiment: Defining the Social Virtual Research Environment
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
Case base mining for adaptation knowledge acquisition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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How to automate case adaptation is a classic problem for case-based reasoning. Given the difficulty of developing reliable case adaptation methods, it is appealing to consider methods which can exploit the strengths of a set of alternative adaptation methods. This paper presents a framework for combining suggestions from multiple adaptation methods, and illustrates and evaluates the approach in the context of interactive support for user modification of scientific workflows. The paper presents four adaptation methods for this domain, describes a method for assessing their confidence, proposes four strategies for suggestion combination, and evaluates the performance of the approach. The evaluation suggests that, for this domain, results depend more strongly on the adaptation methods chosen than on the specific combination method used, and that they depend especially strongly on a confidence threshold used for limiting irrelevant and incorrect suggestions.