Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

High-dimensional multilabel data have increasingly emerged in many application areas,
suffering from two noteworthy issues: instances with high-dimensional features and large …

Multi-label feature selection is an indispensable technology in multi-semantic high-
dimensional data preprocessing, which has been brought into focus in recent years …

Label distribution covers a certain number of labels, representing the degree to which each
label describes the instance. When dealing with label ambiguity, label distribution could …

Feature selection used for dimensionality reduction of the feature space plays an important
role in multi-label learning where high-dimensional data are involved. Although most …

Feature selection, as an important pre-processing technique, can efficiently mitigate the
issue of “the curse of dimensionality” by selecting discriminative features especially for multi …

Label distribution offers more information about label polysemy than logical label. There are
presently two approaches to obtaining label distributions: LDL (label distribution learning) …

As an important preprocessing procedure, dimensionality reduction for multi-label learning
is an effective way to solve the challenge caused by high-dimensionality data. Most existing …

Multi-label learning (MLL) refers to the problem of tagging a given instance with a set of
relevant labels. In MLL, the implicit relative importance of different labels representing a …

Background Asthma is a chronic respiratory disease affecting millions of people worldwide,
but early detection can be challenging due to the time-consuming nature of the traditional …