Naik / Rangwala Large Scale Hierarchical Classification: State of the Art

1. Auflage 2018
ISBN: 978-3-030-01620-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 93 Seiten, eBook

Reihe: SpringerBriefs in Computer Science

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 hierarchy2. 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 forimproving 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.

Zielgruppe

Research

Autoren/Hrsg.

Naik, Azad

Rangwala, Huzefa

Inhaltsverzeichnis

1 Introduction.- 2 Background and Literature Review.- 3 Hierarchical Structure Inconsistencies.- 4 Large Scale Hierarchical Classification with Feature Selection.- 5 Multi-Task Learning.- 6 Conclusions and Future Research Directions.