Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multi-label classifica...

Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to exponential growth of output space. It confronts great challenge for exploration latent label relationship and intrinsic correlation between feature spaces. MLC gave rise a framework named compression (LC) obtain compact space efficient learning. Nevertheless, mos...

It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are sh...

Multi-label data classification has become an important and active research topic, where the classification algorithm is required to deal with prediction of sets of label indicators for instances simultaneously. Label powerset (LP) method reduces the multi-label classification problem to a single-label multi-class classification problem by treating each distinct combination of labels. However, ...