A large body of research in supervised learning deals with the analysis of single-label data,
where training examples are associated with a single label λ from a set of disjoint labels L …

Automated classification of text into predefined categories has always been considered as a
vital method to manage and process a vast amount of documents in digital forms that are …

Efficient distributed numerical word representation models (word embeddings) combined
with modern machine learning algorithms have recently yielded considerable improvement …

Hierarchical multi-label text classification (HMTC) is a fundamental but challenging task of
numerous applications (eg, patent annotation), where documents are assigned to multiple …

In this survey we discuss the task of hierarchical classification. The literature about this field
is scattered across very different application domains and for that reason research in one …

Text categorization is a significant tool to manage and organize the surging text data. Many
text categorization algorithms have been explored in previous literatures, such as KNN …

Feature selection is an important task in machine learning, which can effectively reduce the
dataset dimensionality by removing irrelevant and/or redundant features. Although a large …

In multi-label learning, since different labels may have some distinct characteristics of their
own, multi-label learning approach with label-specific features named LIFT has been …

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 …

Each example in a multi-label dataset is associated with multiple labels, which are often
correlated. Learning from this data can be improved when dimensionality reduction tasks …