Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14828
Title: Using fuzzy integral to modeling case based reasoning with feature interaction
Authors: Wang, XZ
Yeung, DS
Keywords: Case-based reasoning
Fuzzy set theory
Issue Date: 2000
Publisher: IEEE
Source: 2000 IEEE International Conference on Systems, Man, and Cybernetics, October 2000, Nashville, TN, v. 5, p. 3660-3665 How to cite?
Abstract: The guiding principle of case-based reasoning (CBR) is the CBR-hypothesis which assumes that “similar problems have similar solutions”. This principle requires a model to compute the problem-similarity in terms of individual features. One frequently used model is to consider the weighted average of feature-similarities as an overall similarity measure. Due to some inherent interaction among diverse features, the weighted average model does not work well in many real-world problems. This paper proposes using a non-linear integral tool to address such a problem. Five fuzzy integrals with respect to a fuzzy measure or a nonadditive set function are discussed in this paper. The interaction among the features is considered to be reflected in the non-additive set function, and the overall similarity is computed by using the integral model instead of using the weighted average model. Because the weighted average can be regarded as a special case of nonlinear integral, this paper to some extent generalizes the application scope of traditional CBR techniques based on similarity
URI: http://hdl.handle.net/10397/14828
ISBN: 0-7803-6583-6
ISSN: 1062-922X
DOI: 10.1109/ICSMC.2000.886578
Appears in Collections:Conference Paper

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