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Using fuzzy integral to modeling case based reasoning with feature interaction

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2 Author(s)
Wang, X.Z. ; Dept. of Comput., Hong Kong Polytech., Kowloon, China ; Yeung, D.S.

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

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Systems, Man, and Cybernetics, 2000 IEEE International Conference on  (Volume:5 )

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