Multi-view multi-label learning has attracted the attention of many scholars and widely used
in multiple fields. While in real-world applications, due to the lack of manpower and …

Many important classification problems in the real-world consist of a large number of closely
related categories in a hierarchical structure or taxonomy. Hierarchical multi-label text …

Data set with incomplete information, multi-granularity label correlation when label-specific
features and complementarity information provided is ubiquitous in real-world applications …

The label-specific features learning that extracts specific features from different labels for
classification has attracted wide attention in recent years. The strategy of label-specific …

In multi-label learning, each object is represented by a single instance and associated with
multiple labels simultaneously. Existing multi-label learning approaches mainly construct …

Multi-label classification aims at finding more than one label for each instance. The
performance of multi-label classifiers is reduced when faced with high-dimensional tasks …

Each instance in multi-label data is associated with multiple labels, and there are irrelevant
or redundant features in its feature space, which leads to the performance degradation of …

In many real-world problems, instances belong to multiple semantic concepts
simultaneously, where they may be characterized by an uncertain association or a full …

Les avancées technologiques de la vision par ordinateur et l'utilisation des systèmes
d'intelligence artificielle (comme l'apprentissage automatique ou profond) ont eu un fort …