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This paper presents an effective and efficient approach for translating fuzzy classification rules that use approximative sets to rules that use descriptive sets and linguistic hedges of predefined meaning. It works by first generating rules that use approximative sets from training data, and then translating the resulting approximative rules into descriptive ones. Hedges that are useful for supporting such translations are provided. The translated rules are functionally equivalent to the original approximative ones, or a close equivalent given search time restrictions, while reflecting their underlying preconceived meaning. Thus, fuzzy, descriptive classifiers can be obtained by taking advantage of any existing approach to approximative modeling, which is generally efficient and accurate, while employing rules that are comprehensible to human users. Experimental results are provided and comparisons to alternative approaches given.