Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Machine Learning
The Utility Problem Analysed: A Case-Based Reasoning Perspective
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
A Hybrid Case Based Reasoning Approach for Wine Classification
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
On diversity and accuracy of homogeneous and heterogeneous ensembles
International Journal of Hybrid Intelligent Systems
Hybrid algorithms with instance-based classification
ECML'05 Proceedings of the 16th European conference on Machine Learning
The utility problem for lazy learners - towards a non-eager approach
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
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In this paper we present an efficient hybrid classification algorithm based on combining case-based reasoning and random decision trees, which is based on a general approach for combining lazy and eager learning methods. We use this hybrid classification algorithm to predict the pain classification for palliative care patients, and compare the resulting classification accuracy to other similar algorithms. The hybrid algorithm consistently produces a lower average error than the base algorithms it combines, but at a higher computational cost.