Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Induction of fuzzy decision trees
Fuzzy Sets and Systems
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
Learning optimization in simplifying fuzzy rules
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
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
Maintaining Case-Based Reasoning Systems Using Fuzzy Decision Trees
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Case-Based Reasoning in Color Matching
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Case-Based Reasoning Technology, From Foundations to Applications
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
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
Automated Case Generation from Databases Using Similarity-Based Rough Approximation
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Noise reduction for instance-based learning with a local maximal margin approach
Journal of Intelligent Information Systems
Journal of Computer and System Sciences
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This paper proposes a fuzzy-rough method of maintaining Case-Based Reasoning (CBR) systems. 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 take the form of fuzzy rules generated by the rough set technique. In paper [1], we have proposed a methodology for case base maintenance which used a fuzzy decision tree induction to discover the adaptation rules; in this paper, we focus on using a heuristic algorithm, i.e., a fuzzy-rough algorithm [2] in the process of simplifying fuzzy rules. This heuristic, regarded as a new fuzzy learning algorithm, has many significant advantages, such as rapid speed of training and matching, generating a family of fuzzy rules which is approximately simplest. By applying such a fuzzy-rough learning algorithm to the adaptation mining phase, the complexity of case base maintenance is reduced, and the adaptation knowledge is more compact and effective. The effectiveness of the method is demonstrated experimentally using two sets of testing data, and we also compare the maintenance results of using fuzzy ID3, in [1], and the fuzzy-rough approach, as in this paper.