Multi-label classification has gained significant attention during recent years, due to the increasing number of modern applications associated with multi-label data. Despite its short life, different approaches have been presented to solve the task of multi-label classification. LIFT is a multi-label classifier which utilizes a new strategy to multi-label learning by leveraging label-specific ...

Multi-label classification has many applications in the text categorization, biology and medical diagnosis, in which multiple class labels can be assigned to each training instance simultaneously. As it is often the case that there are relationships between the labels, extracting the existing relationships between the labels and taking advantage of them during the training or prediction phases ...

Multi-label text classification has been widely concerned by scholars due to its contribution practical applications. One of the key challenges in multi-label is how extract and leverage correlation among labels. However, it quite challenging directly model correlations labels a complex unknown label space. In this paper, we propose Label Prompt Text Classification (LP-MTC), which inspired idea...

An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns. One way of learning underlying structures over labels is to project both instances and labels into the same space where an instance and its relevant labels tend to have similar representations. In this paper, we present a novel method to learn a joint sp...

In classification problems, a pattern may belong to one or multiple categories. It is essential to deal multi-label classification accurately and efficiently. Threshold strategies can be used for multi-label classification. We propose four schemes to compute threshold for a threshold based multi-label classification. We validate our method using multi-label text data and multi-label image data....