Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
An algorithm for computing the transitive closure of a fuzzy similarity matrix
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue on fuzzy optimization
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
FILM: a fuzzy inductive learning method for automated knowledge acquisition
Decision Support Systems - Special issue: expertise and modeling expert decision making
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning
Modelling the Competence of Case-Bases
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
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
Learning Adaptation Rules from a Case-Base
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
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
A Fuzzy-Rough Approach for Case Base Maintenance
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Trend discovery in financial time series data using a case based fuzzy decision tree
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
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This paper proposes a methodology of maintaining Case Based Reasoning (CBR) systems by using fuzzy decision tree induction - a machine learning technique. The methodology is mainly based on the idea that a large case library can be transformed to a small case library together with a group of adaptation rules, which are generated by fuzzy decision trees. Firstly, an approach to learning feature weights automatically is used to evaluate the importance of different features in a given case-base. Secondly, clustering of cases will be carried out to identify different concepts in the case-base using the acquired feature knowledge. Thirdly, adaptation rules will be mined for each concept using fuzzy decision trees. Finally, a selection strategy based on the concepts of Ɛ-coverage and Ɛ-reachability is used to select representative cases. The effectiveness of the method is demonstrated experimentally using two sets of testing data.