Abstract
Preference analysis is a class of important tasks in multi-criteria decision making. The classical rough set theory was generalized to deal with preference analysis by replacing equivalence relations with dominance relation. However, crisp preference relations can not reflect the fuzziness in criteria. In this paper, we introduce the logsig function to extract fuzzy preference relations from samples characterized with numerical attributes. Then we integrate fuzzy preference relations with an improved fuzzy rough set model and develop a fuzzy preference rough set model. We generalize the dependency used in classical rough sets and fuzzy rough sets to compute the relevance between the criteria and decision. The proposed model is used to analyze a fuzzy preference data. It shows the effectiveness of the proposed model.
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