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A framework is proposed for learning fuzzy rule-based systems from low quality data where the differences between observed and true values may introduce systematic bias in the model. It is argued that there are problems where aggregating imprecise losses into numerical or fuzzy-valued risk functions discards useful information, thus generalizing the risk of a model to a vector of fuzzy losses is preferred...
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