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

A common approach to constructing fuzzy rough sets (FRSs) is using t-norms. Furthermore,
establishing multi-granulation fuzzy rough sets (MGFRSs) is also usually undertaken by …

In the era of big data, both the size and the number of features, samples, and classes
continue to increase, resulting in high-dimensional classification tasks. One characteristic …

Hierarchical feature selection has proven to be significant in reducing classification difficulty.
Many existing hierarchical feature selection methods use the hierarchy in the class space as …

Abstract Information entropy theory has been widely studied and successfully applied to
machine learning and data mining. The fuzzy entropy and neighborhood entropy theories …

Automated learning from datasets with a long-tailed distribution has gradually become a
research hotspot due to the increasing complexity of large-scale real-world datasets …

Few-shot learning poses a great challenge for obtaining a classifier that recognizes new
classes from a few labeled examples. Existing solutions perform well by leveraging meta …

For hierarchical classification tasks, label relationships can be represented as a hierarchical
structure ranging from coarse-grained to fine-grained. Existing hierarchical classifications …

In hierarchical classification learning, the feature space of data has high dimensionality and
is unknown with emergent features. To solve the above problems, we propose an online …

Multi-task feature selection (MTFS) has been proven effective for reducing the curse of
dimensionality in large-scale classification. Many existing MTFS methods assume that all …