Fuzzy sets and applications
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Case-based reasoning
The ordered weighted averaging operators: theory and applications
The ordered weighted averaging operators: theory and applications
On the inclusion of importances in OWA aggregations
The ordered weighted averaging operators
On the issue of obtaining OWA operator weights
Fuzzy Sets and Systems
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Soft computing in case based reasoning
Soft computing in case based reasoning
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Fuzzy Sets and Systems - Special issue: Preference modelling and applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Induced ordered weighted averaging operators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using fuzzy methods to model nearest neighbor rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy methods in machine learning and data mining: Status and prospects
Fuzzy Sets and Systems
Hybrid model for learner modelling and feedback prioritisation in exploratory learning
International Journal of Hybrid Intelligent Systems - CIMA-08
A case-based classifier for hypertension detection
Knowledge-Based Systems
Fuzzy sets in machine learning and data mining
Applied Soft Computing
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Fuzzy rule-based similarity model enables learning from small case bases
Applied Soft Computing
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Our goal is to provide some tools, based on soft computing aggregation methods, useful in the two fundamental steps in case base reasoning, matching the target and the cases and fusing the information provided by the relevant cases. To aid in the first step we introduce a methodology for matching the target and cases which uses a hierarchical representation of the target object. We also introduce a method for fusing the information provided by relevant retrieved cases. This approach is based upon the nearest neighbor principle and uses the induced ordered weighted averaging operator as the basic aggregation operator. A procedure for learning the weights is described.