Hierarchical classification (HC) is effective when categories are organized hierarchically.
However, the blocking problem makes the effect of hierarchical classification greatly …

In this paper, we propose a hierarchical regularization framework for large-scale hierarchical
classification. In our framework, we use the regularization structure to share information …

Recent large-scale hierarchical classification tasks typically have tens of thousands of
classes on which the most widely used approach to multiclass classification--one-versus …

The sizes of datasets in terms of the number of samples, features, and classes have
dramatically increased in recent years. In particular, there usually exists a hierarchical …

Hierarchical classification is a challenging problem where the class labels are organized in
a predefined hierarchy. One primary challenge in hierarchical classification is the small …

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

Classification and recognition tasks confronted by intelligent systems are becoming
complicated as the sizes of samples, dimensionality and labels dramatically increase in the …

Hierarchical Classification (HC) is a supervised learning problem where unlabeled
instances are classified into a taxonomy of classes. Several methods that utilize the …

In the era of big data, the amount of class labels is growing rapidly, which poses a great
challenge to classification tasks. The hierarchical classification was thus introduced to …

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