Multi-label learning is a class of machine learning algorithms that study the classification
problem of data associated with multiple labels simultaneously. Ensemble-based method is …

Multi-label learning on real-world data is a challenging task due to sparse labels, missing
labels, and sparse structures. Some existing approaches are effective in addressing the …

Multi-label learning tasks usually encounter the problem of the class-imbalance, where
samples and their corresponding labels are non-uniformly distributed over multi-label data …

The explosive growth of datasets is always accompanied by dimension disasters, which
have become more common in multi-label data. Various feature selection techniques are …

Numerous high-dimension multilabel data are generated, posing a challenge for multilabel
learning. Building effective learning models with discriminative features is essential to …

Feature selection is a key technique to tackle the curse of dimensionality in multi-label
learning. Lots of embedded multi-label feature selection methods have been developed …

In partial multi-label learning (PML), each data example is equipped with a candidate label
set, which consists of multiple ground-truth labels and other false-positive labels. Recently …

In conventional multi-label learning, each training instance is associated with multiple
available labels. Nevertheless, real-world objects usually exhibit more sophisticated …

The identification of sexual activities in images can be helpful in detecting the level of
content severity and can assist pornography detectors in filtering specific types of content. In …

Similar to single-label learning, multi-label learning employs feature selection technique to
alleviate the curse of dimensionality. Many multi-label methods, which utilize label …