In multi-label learning, since different labels may have their own characteristics, multi-label learning approach with label-specific features named LIFT has been proposed. However, the construction of label-specific features may increase the dimension of feature vector, which brings some redundant information in feature space. To solve this problem, a multi-label learning approach named FRS-LIFT was presented, which can implement label-specific feature reduction by fuzzy rough set. FRS-LIFT contains four steps: construction of label-specific features, reduction of feature dimensionality, training of classification models and prediction of unknown samples. The experimental results on 5 multi-label datasets show that, compared with LIFT, the proposed method can not only reduce the dimension of label-specific features, but also achieve satisfactory performances in 5 evaluation metrics.