In multi-label classification, the main focus has been to develop ways of learning the
underlying dependencies between labels, and to take advantage of this at classification …

Multi-label classification is a generalization of binary classification where the task consists in
predicting\emphsets of labels. With the availability of ever larger datasets, the multi-label …

Multi-label classification is a practical yet challenging task in machine learning related fields,
since it requires the prediction of more than one label category for each input instance. We …

In multi-label classification, the strategy of label-specific features has been shown to be
effective to learn from multi-label examples by accounting for the distinct discriminative …

In multi-label classification, an instance may be associated with a set of labels
simultaneously. Recently, the research on multi-label classification has largely shifted its …

Many real-world applications require multi-label classification where multiple target labels
are assigned to each instance. In multi-label classification, there exist the intrinsic …

In the “classifier chains”(CC) approach for multi-label classification, the predictions of binary
classifiers are cascaded along a chain as additional features. This method has attained high …

Label-specific features serve as an effective strategy to learn from multi-label data with
tailored features accounting for the distinct discriminative properties of each class label …

scikit-multilearn is a Python library for performing multi-label classification. The library is
compatible with the scikit/scipy ecosystem and uses sparse matrices for all internal …

Abstract Representation learning is one of the most important aspects of multi-label learning
because of the intricate nature of multi-label data. Current research on representation …