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Multilabel Classification

Problem Analysis, Metrics and Techniques

  • Francisco Herrera
  • Francisco Charte
  • Antonio J. Rivera
  • María J. del Jesus

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 1-16
  3. Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 17-31
  4. Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 33-63
  5. Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 65-79
  6. Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 81-99
  7. Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 101-113
  8. Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 115-131
  9. Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 133-151
  10. Francisco Herrera, Francisco Charte, Antonio J. Rivera, María J. del Jesus
    Pages 153-191
  11. Back Matter
    Pages 193-194

About this book

Introduction

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.

Keywords

Data mining Machine learning Classification Multi-label data Text categorization Preprocessing Dataset characterization Learning from imbalanced data Dimensionality reduction Feature selection Data mining software

Authors and affiliations

  • Francisco Herrera
    • 1
  • Francisco Charte
    • 2
  • Antonio J. Rivera
    • 3
  • María J. del Jesus
    • 4
  1. 1.University of GranadaGranadaSpain
  2. 2.University of GranadaGranadaSpain
  3. 3.University of JaénJaénSpain
  4. 4.University of JaénJaénSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-41111-8
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-41110-1
  • Online ISBN 978-3-319-41111-8
  • Buy this book on publisher's site