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Multilabel Classification

Problem Analysis, Metrics and Techniques

  • Book
  • © 2016

Overview

Authors:
  1. Francisco Herrera

    1. University of Granada, Granada, Spain

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    You can also search for this author in PubMed   Google Scholar

  2. Francisco Charte

    1. University of Granada, Granada, Spain

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  3. Antonio J. Rivera

    1. University of Jaén, Jaén, Spain

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  4. María J. del Jesus

    1. University of Jaén, Jaén, Spain

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  • Covers key concepts of multilabel data characterization, treatment, and evaluation
  • Equips the reader with all the software tools needed to handle multilabel data, including step-by-step instructions for use
  • Provides the perfect guide for beginners and practitioners with interest in the topic, as well as experts seeking a comprehensive overview
  • Includes supplementary material: sn.pub/extras

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Introduction

    • Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 1-16

  3. Multilabel Classification

    • Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 17-31

  4. Case Studies and Metrics

    • Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 33-63

  5. Transformation-Based Classifiers

    • Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 65-79

  6. Adaptation-Based Classifiers

    • Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 81-99

  7. Ensemble-Based Classifiers

    • Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 101-113

  8. Dimensionality Reduction

    • Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 115-131

  9. Imbalance in Multilabel Datasets

    • Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 133-151

  10. Multilabel Software

    • Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 153-191

  11. Back Matter

    Pages 193-194
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Keywords

About this book

This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are:


• The special characteristics of multi-labeled data and the metrics available to measure them.
• The importance of taking advantage of label correlations to improve the results.
• The different approaches followed to face multi-label classification.
• The preprocessing techniques applicable to multi-label datasets.
• The available software tools to work with multi-label data.


This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.


Authors and Affiliations

  • University of Granada, Granada, Spain

    Francisco Herrera, Francisco Charte

  • University of Jaén, Jaén, Spain

    Antonio J. Rivera, María J. del Jesus

Bibliographic Information

  • Book Title: Multilabel Classification

  • Book Subtitle: Problem Analysis, Metrics and Techniques

  • Authors: Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus

  • DOI: https://doi.org/10.1007/978-3-319-41111-8

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer International Publishing Switzerland 2016

  • Hardcover ISBN: 978-3-319-41110-1Published: 22 August 2016

  • Softcover ISBN: 978-3-319-82269-3Published: 22 April 2018

  • eBook ISBN: 978-3-319-41111-8Published: 09 August 2016

  • Edition Number: 1

  • Number of Pages: XVI, 194

  • Number of Illustrations: 72 b/w illustrations

  • Topics: Data Mining and Knowledge Discovery, Artificial Intelligence

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