In many real-world multi-label applications, the content of multi-label data is usually
characterized by high dimensional features, which contains complex correlation information …

Information theoretical-based methods have attracted a great attention in recent years and
gained promising results for multilabel feature selection (MLFS). Nevertheless, most of the …

Recent years, joint feature selection and multi-label learning have received extensive
attention as an open problem. However, there exist three general issues in previous multi …

Class imbalance is a common issue in the community of machine learning and data mining.
The class-imbalance distribution can make most classical classification algorithms neglect …

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 …

In the multi-label classification task, an instance is simultaneously associated with multiple
semantic labels. Due to the high complexity of the semantic space in practical applications …

Partial multi-label learning refers to the problem that each instance is associated with a
candidate label set involving both relevant and noisy labels. Existing solutions mainly focus …

Large-scale multiobjective optimization problems (LSMOPs) are characterized as
optimization problems involving hundreds or even thousands of decision variables and …

The relationship between features and labels is an important factor affecting multi-label
learning. Many existing multi-label learning approaches decompose label space into a low …