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 …

Real-life objects typically contain complex structures, and the graph is a prevalent
presentation for describing such objects. Multi-graph multi-label (MGML) learning is a …

The last decade has witnessed a continuous boom in the application of machine learning
techniques in pattern recognition, with much more focus on single-task learning models …

Multi-label learning has attracted more and more researchers' attention. It deals with the
problem where each instance is associated with multiple labels simultaneously. Some …

In multi-view multi-label learning, each object is represented by multiple data views, and
belongs to multiple class labels simultaneously. Generally, all the data views have a …

Learning label-specific features is an effective strategy for multi-label classification. Existing
multi-label classification methods for learning label-specific features face two challenges …

In multi-label learning, each instance is assigned by several nonexclusive labels. However,
these labels are often incomplete, resulting in unsatisfactory performance in label related …

Label missing and class imbalance problems are two hot research topics in machine
learning, and they have been impeding the improvement of model performance, especially …

Multi-label feature selection serves an effective dimensionality reduction technique in the
high-dimensional multi-label data. However, most feature selection methods regard the …

In multi-label learning, samples of practical classification task may associated with multiple
labels, it is challenging to acquire all labels of the training samples, the rapid expansion of …