Label distribution learning (LDL) is an emerging framework in machine learning. Fuzzy
mutual information is mutual information under a fuzzy environment and plays an important …

Nowadays, multi-label data are ubiquitous in real-world applications, in which each instance
is associated with a set of labels. Multi-label learning has attracted significant attentions from …

Multi-view multi-label learning (MVML) is an important paradigm in machine learning, where
each instance is represented by several heterogeneous views and associated with a set of …

In multilabel classification, each sample can be allocated to multiple class labels at the same
time. However, one of the prominent problems of multilabel classification is missing labels …

Distance-based supervised method, the minimal learning machine, constructs a predictive
model from data by learning a mapping between input and output distance matrices. In this …

Missing labels in multi-label datasets are a common problem, especially for minority classes,
which are more likely to occur. This limitation hinders the performance of classifiers in …

Label-specific features learning can effectively exploit the unique features of each label,
which alleviates the high dimensionality and improves the classification performance of multi …

The relationship between features and labels, as well as the correlation between labels are
two important factors that affect multi-label learning. Many existing studies assume that there …

The typical inaccuracy of data gathering and preparation procedures makes erroneous and
unnecessary information to be a common issue in real-world applications. In this context …

The vast amount of data is key challenge to mine a new scholar that is plausible to be star in
the upcoming period. The enormous amount of unstructured data raise every year is …