EAN: 9783030016197

SPRINGERBRIEFS IN COMPUTER SCIENCE LARGE SCAL...

This SpringerBrief covers the technical material related to large scale hierarchical
classification (LSHC). HC is an important machine learning problem that has been researched and
explored extensively in the past few years. In this book  the authors provide a comprehensive
overview of various state-of-the-art existing methods and algorithms that were developed to
solve the HC problem in large scale domains. Several challenges faced by LSHC is discussed in
detail such as: 1. High imbalance between classes at different levels of the hierarchy 2.
Incorporating relationships during model learning leads to optimization issues 3. Feature
selection 4. Scalability due to large number of examples  features and classes 5. Hierarchical
inconsistencies 6. Error propagation due to multiple decisions involved in making predictions
for top-down methods The brief also demonstrates how multiple hierarchies can be leveraged for
improving the HC performance using different Multi-Task Learning (MTL) frameworks. The purpose
of this book is two-fold: 1. Help novice researchers beginners to get up to speed by providing
a comprehensive overview of several existing techniques. 2. Provide several research directions
that have not yet been explored extensively to advance the research boundaries in HC. New
approaches discussed in this book include detailed information corresponding to the
hierarchical inconsistencies  multi-task learning and feature selection for HC. Its results are
highly competitive with the state-of-the-art approaches in the literature.

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