In the framework of multi-label learning, each example is represented by a single instance (feature vector) while simultaneously associated with multiple class labels. A common strategy adopted by most existing multi-label learning algorithms is that the very feature set of each example is employed in the discrimination processes of all class labels. However, this popular strategy might be suboptimal as each label is supposed to possess specific characteristics of its own. Based on this assumption, a multi-label learning algorithm named LIFT is proposed, in which label specific feature of each label is utilized in the discrimination process of the corresponding label. LIFT contains two steps: label-specific features construction and classification models induction. LIFT constructs the label-specific features by querying the clustering results and then induces the classification model with the corresponding label-specific features. In this paper, three variants of LIFT are studied, all employ other label-specific feature construction mechanisms while retaining the classification models induction process of LIFT. To validate the general helpfulness of label-specific feature mechanism to multi-label learning and the effectiveness of those label-specific features adopted by LIFT, two groups of experiments are conducted on a total of twelve multi-label benchmark datasets.