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Large Scale Hierarchical Classification: State of the Art

  • Azad Naik
  • Huzefa Rangwala

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Azad Naik, Huzefa Rangwala
    Pages 1-11
  3. Azad Naik, Huzefa Rangwala
    Pages 13-38
  4. Azad Naik, Huzefa Rangwala
    Pages 39-59
  5. Azad Naik, Huzefa Rangwala
    Pages 75-88
  6. Azad Naik, Huzefa Rangwala
    Pages 89-93

About this book

Introduction

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.


Keywords

hierarchical classification hierarchical inconsistencies data mining artificial intelligence large scale

Authors and affiliations

  • Azad Naik
    • 1
  • Huzefa Rangwala
    • 2
  1. 1.Microsoft (United States)RedmondUSA
  2. 2.George Mason UniversityFairfaxUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-01620-3
  • Copyright Information The Author(s), under exclusive license to Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-01619-7
  • Online ISBN 978-3-030-01620-3
  • Series Print ISSN 2191-5768
  • Series Online ISSN 2191-5776
  • Buy this book on publisher's site