In many real-world application domains, eg, text categorization and image annotation,
objects naturally belong to more than one class label, giving rise to the multi-label learning …

Multi-label feature selection is an efficient technique to deal with the high dimensional multi-
label data by selecting the optimal feature subset. Existing researches have demonstrated …

Hierarchical classification learning, which organizes data categories into a hierarchical
structure, is an effective approach for large-scale classification tasks. The high …

Multi-label feature selection can efficiently handle large amounts of multi-label data.
However, two pressing issues remain in sparse learning for multi-label data. First, many …

Feature selection can alleviate the problem of the curse of dimensionality by selecting more
discriminative features, which plays an important role in multi-label learning. Recently …

Prevalent multi-label feature selection (MLFS) approaches to obtain the most suitable
feature subset by dealing with two issues, namely sparsity and redundancy. In this paper, we …

Multi-label data classification has received much attention due to its wide range of
application domains. Unfortunately, a class imbalance problem often occurs in multi-label …

Since Electroencephalogram (EEG) is resistant to camouflage, it has been a reliable data
source for objective emotion recognition. EEG is naturally multi-rhythm and multi-channel …

In the domain of multi-view multi-label (MVML) learning, features are distributed across
various views, each offering multiple semantic representations. While existing approaches …

Background Methylphenidate is an effective first-line treatment for attention-
deficit/hyperactivity disorder (ADHD). However, many adverse effects of methylphenidate …