Instance-based prediction of real-valued attributes
Computational Intelligence
Instance-Based Learning Algorithms
Machine Learning
The three semantics of fuzzy sets
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Case Based Reasoning, Fuzzy Systems Modeling and Solution Composition
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Toward a probabilistic formalization of case-based inference
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
A logical approach to case-based reasoning using fuzzy similarity relations
Information Sciences: an International Journal
A New Perspective on Reasoning with Fuzzy Rules
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Possibilistic network-based classifiers: on the reject option and concept drift issues
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
On learning similarity relations in fuzzy case-based reasoning
Transactions on Rough Sets II
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The "similar problem-similar solution" hypothesis underlying case-based reasoning is modelled in the framework of possibility theory and fuzzy sets. Thus, case-based prediction can be realized in the form of fuzzy set-based approximate reasoning. The inference process makes use of fuzzy rules. It is controlled by means of modifier functions actingo n such rules and related similarity measures. Our approach also allows for the incorporation of domain-specific (expert) knowledge concerning the typicality (or exceptionality) of the cases at hand. It thus favors a view of case-based reasoning accordingto which the user interacts closely with the system in order to control the generalization beyond observed data. Our method is compared to instance-based learning and kernel-based density estimation. Loosely speaking, it adopts basic principles of these approaches and supplements them with the capability of combining knowledge and data in a flexible way.