The problem of classification has been widely studied in the data mining, machine learning,
database, and information retrieval communities with applications in a number of diverse …

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 …

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

Classification of data is difficult if the data is imbalanced and classes are overlap**. In
recent years, more research has started to focus on classification of imbalanced data since …

Web search result clustering aims to facilitate information search on the Web. Rather than
the results of a query being presented as a flat list, they are grouped on the basis of their …

The two key challenges in hierarchical classification are to leverage the hierarchical
dependencies between the class-labels for improving performance, and, at the same time …

Text-based document geolocation is commonly rooted in language-based information
retrieval techniques over geodesic grids. These methods ignore the natural hierarchy of …

Traditional flat classification methods (eg, binary or multi-class classification) neglect the
structural information between different classes. In contrast, Hierarchical Multi-label …

In this paper, we present a novel approach to Web search result clustering based on the
automatic discovery of word senses from raw text, a task referred to as Word Sense …

Multi-label text classification refers to the problem of assigning each given document its most
relevant labels from a label set. Commonly, the metadata of the given documents and the …