Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
Selecting weighting factors in logarithmic opinion pools
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diversity versus Quality in Classification Ensembles Based on Feature Selection
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Error-Correcting Output Codes for Local Learners
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Nearest Neighbors in Random Subspaces
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
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
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Adaptive mixtures of local experts
Neural Computation
Explanation in Case-Based Reasoning---Perspectives and Goals
Artificial Intelligence Review
A multiple-criteria quadratic programming approach to network intrusion detection
CASDMKM'04 Proceedings of the 2004 Chinese academy of sciences conference on Data Mining and Knowledge Management
| Hi-index | 0.00 |
Ensemble research has shown that the aggregated output of an ensemble of predictors can be more accurate than a single predictor. This is true also for lazy learning systems like Case-Based Reasoning (CBR) and k-Nearest-Neighbour. Aggregation is normally achieved by voting in classification tasks and by averaging in regression tasks. For CBR, this increased accuracy comes at the cost of interpretability however. If we consider the use of retrieved cases for explanation to be one of the advantages of CBR then this is lost in an ensemble. This is because a large number of cases will have been retrieved by the ensemble members. In this paper we present a new technique for aggregation that obtains excellent results and identifies a small number of cases for use in explanation. This new approach might be viewed as a transformation process whereby cases are transformed from their feature based representation to a representation based on the predictions of ensemble members. This new representation produces very accurate predictions and allows a small number of similar neighbours to be identified.