The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

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

The classification task usually works with flat and batch learners, assuming problems as
stationary and without relations between class labels. Nevertheless, several real-world …

Visual text analytics has recently emerged as one of the most prominent topics in both
academic research and the commercial world. To provide an overview of the relevant …

Moving towards the next generation of personalized learning environments requires
intelligent approaches powered by analytics for advanced learning contexts with enriched …

The human mind grows in learning new knowledge, which finally organizes and develops a
basic mental pattern called cognitive structure. Hierarchical multi-label text classification …

A guide to the principles and methods of data analysis that does not require knowledge of
statistics or programming A General Introduction to Data Analytics is an essential guide to …

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

Text Classification pipelines are a sequence of tasks needed to be performed to classify
documents into a set of predefined categories. The pre-processing phase (before training) of …

This paper investigates recent research on active learning for (geo) text and image
classification, with an emphasis on methods that combine visual analytics and/or deep …