Weighted fuzzy production rules
Fuzzy Sets and Systems
Unsupervised feature selection using a neuro-fuzzy approach
Pattern Recognition Letters
Dynamically Creating Indices for Two Million Cases: A Real World Problem
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Modelling the Competence of Case-Bases
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Learning a Local Similarity Metric for Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Case-Based Reasoning in Color Matching
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Discovering Case Knowledge Using Data Mining
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Probability Based Metrics for Nearest Neighbor Classification and Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Categorizing Case-Base Maintenance: Dimensions and Directions
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
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
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In this paper we proposed an approach to maintain large case library, which based on the idea that a large case library can be transformed to a compact one by using a set of case-specific weights. A linear programming technique is being used to obtain case-specific weights. By learning such local weights knowledge, many of redundant or similar cases can be removed from the original case library or stored in a secondary case library. This approach is useful for case library with a large number of redundant or similar cases and the retrieval efficiency is a real concern of the user. This method of maintaining case library from scratch, as proposed in this paper, consists of two main steps. First, a linear programming technique for learning case-specific weights is used to evaluate the importance of different features for each case. Second, a case selection strategy based on the concepts of case coverage and reachability is carried out to select representative cases. Furthermore, a case retrieval strategy of the compact case library we built is discussed. The effectiveness of the approach is demonstrated experimentally by using two sets of testing data, and the results are promising.