Aiming at the problem of multi-label classification,a multi-label classification algorithm based on label-specific features is proposed in this paper.In this algorithm,we compute feature density on the positive and negative instances set of each class firstly and then select mk features of high density from the positive and negative instances set of each class,respectively;the intersection is taken as the label-specific features of the corresponding class.Finally,multi-label data are classified on the basis of label-specific features.The algorithm can show the label-specific features of each class.Experiments show that our proposed method,the MLSF algorithm,performs significantly better than the other state-of-the-art multi-label learning approaches. Aiming at the problem of multi-label classification, a multi-label classification algorithm based on label-specific features is proposed in this paper. In this algorithm, we compute feature density on the positive and negative instances set of each class first and then select mk features of high density from the positive and negative instances set of each class, respectively; the intersection is taken as the label-specific features of the corresponding class. Finally, multi-label data are classified on the basis of label-specific features. The algorithm can show the label-specific features of each class. Experiments show that our proposed method, the MLSF algorithm, performs significantly better than the other state-of-the-art multi-label learning approaches.