Case-based reasoning
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
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
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
Remembering to Add: Competence-preserving Case-Addition Policies for Case Base Maintenance
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Towards Dynamic Maintenance of Retrieval Knowledge in CBR
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Kernel independent component analysis
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Case-based similarity assessment: estimating adaptability from experience
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Credible Case-Based Inference Using Similarity Profiles
IEEE Transactions on Knowledge and Data Engineering
Mining competent case bases for case-based reasoning
Artificial Intelligence
Spatial Point-Data Reduction Using Pulse Coupled Neural Network
Neural Processing Letters
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We present a novel algorithm for extracting a high-quality case base from raw data while preserving and sometimes improving the competence of case-based reasoning. We extend the framework of Smyth and Keane's case-deletion policy with two additional features. First, we build a case base using a statistical distribution that is mined from the input data so that the case-base competence can be preserved or even increased for future problems. Second, we introduce a nonlinear transformation of the data set so that the case-base sizes can be further reduced while ensuring that the competence be preserved and even increased. We show that Smyth and Keane's deletion-based algorithm is sensitive to noisy cases, and that our solution solves this problem more satisfactorily. We show the theoretical foundation and empirical evaluation on several data sets.