De´ja` Vu: a hierarchical case-based reasoning system for software design
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble learning via negative correlation
Neural Networks
Computer science as empirical inquiry: symbols and search
Communications of the ACM
IEEE Transactions on Knowledge and Data Engineering
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
Nearest Neighbors in Random Subspaces
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Case-Based Reasoning Technology, From Foundations to Applications
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
An Approach to Aggregating Ensembles of Lazy Learners That Supports Explanation
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
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
k-NN Aggregation with a Stacked Email Representation
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Maintenance by a Committee of Experts: The MACE Approach to Case-Base Maintenance
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Boosting CBR Agents with Genetic Algorithms
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Expert Systems with Applications: An International Journal
An ensemble of case-based classifiers for high-dimensional biological domains
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Using ensembles of binary case-based reasoners
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
On the use of selective ensembles for relevance classification in case-based web search
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
Ensembles of classifiers will produce lower errors than the member classifiers if there is diversity in the ensemble. One means of producing this diversity in nearest neighbour classifiers is to base the member classifiers on different feature subsets. In this paper we show four examples where this is the case. This has implications for the practice of feature subset selection (an important issue in CBR and data-mining) because it shows that, in some situations, there is no single best feature subset to represent a problem. We show that if diversity is emphasised in the development of the ensemble that the ensemble members appear to be local learners specializing in sub-domains of the problem space. The paper concludes with some proposals on how analysis of ensembles of local learners might provide insight on problem-space decomposition for hierarchical CBR.