Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
Case-based reasoning
Similarity metric learning for a variable-kernel classifier
Neural Computation
Control-Sensitive Feature Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Inside Case-Based Reasoning
Inducing Partially-Defined Instances with Evolutionary Algorithms
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Remembering Why to Remember: Performance-Guided Case-Base Maintenance
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Speed-Up, Quality and Competence in Multi-modal Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Building Compact Competent Case-Bases
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Remembering to Add: Competence-preserving Case-Addition Policies for Case Base Maintenance
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Deleting and Building Sort Out Techniques for Case Base Maintenance
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence
Noise reduction for instance-based learning with a local maximal margin approach
Journal of Intelligent Information Systems
Adaptive case-based reasoning using retention and forgetting strategies
Knowledge-Based Systems
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Case Based Reasoning systems are often faced with the problem of deciding which instances should be stored in the case base. An accurate selection of the best cases could avoid the system being sensitive to noise, having a large memory storage requirements and, having a slow execution speed. This paper proposes two reduction techniques based on Rough Sets theory: Accuracy Rough Sets Case Memory (AccurCM) and Class Rough Sets Case Memory (ClassCM). Both techniques reduce the case base by analysing the representativity of each case of the initial case base and applying a different policy to select the best set of cases. The first one extracts the degree of completeness of our knowledge. The second one obtains the quality of approximation of each case. Experiments using different domains, most of them from the UCI repository, show that the reduction techniques maintain accuracy obtained when not using them. The results obtained are compared with those obtained using well-known reduction techniques.