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In multiple attribute decision making (MADM) problems, decision makers may prefer to classify alternatives into several ordered categories, which is called a classification-based MADM problem. In the process of classification-based MADM, attribute weights play a key role in classification of alternatives due to the fact that different attribute weights may lead to different classification results of alternatives. Thus, a manipulator may strategically set attribute weights to obtain his/her desired classification results of alternatives, which is called classification-based strategic weight manipulation (CBSWM). In this paper, we first give the concept of classification range of alternatives. Subsequently, several mixed 0–1 linear programming models (MLPMs) are constructed to obtain the classification range of alternatives and the strategic attribute weights to obtain a manipulator’s desired classification results of alternatives …