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Multiple Instance Learning

Foundations and Algorithms

  • Francisco Herrera
  • Sebastián Ventura
  • Rafael Bello
  • Chris Cornelis
  • Amelia Zafra
  • Dánel Sánchez-Tarragó
  • Sarah Vluymans

Table of contents

  1. Front Matter
    Pages i-xi
  2. Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 1-16
  3. Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 17-33
  4. Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 35-66
  5. Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 67-98
  6. Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 99-126
  7. Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 127-140
  8. Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 141-167
  9. Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 169-189
  10. Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 191-208
  11. Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 209-230
  12. Back Matter
    Pages 231-233

About this book

Introduction

This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included.

This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined.

Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously. 

This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools.


Keywords

Machine learning Data mining Multiple instance learning Multiple instance classification Multiple instance regression Multiple instance clustering Instance selection in multiple instance learning Dimensionality reduction Feature selection in multiple instance learning Multi-instance learning from imbalanced data Data reduction in multiple instance learning Multi-instance multi-label classification

Authors and affiliations

  • Francisco Herrera
    • 1
  • Sebastián Ventura
    • 2
  • Rafael Bello
    • 3
  • Chris Cornelis
    • 4
  • Amelia Zafra
    • 5
  • Dánel Sánchez-Tarragó
    • 6
  • Sarah Vluymans
    • 7
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  2. 2.Department of Computer ScienceUniversity of CórdobaCórdobaSpain
  3. 3.Center of Information StudiesCentral University “Marta Abreu” of Las VillasSanta ClaraCuba
  4. 4.Department of Applied Mathematics, Computer Science and StatisticsGhent UniversityGhentBelgium
  5. 5.Department of Computer Science and Numerical AnalysisUniversity of CórdobaCórdobaSpain
  6. 6.Central University "Marta Abreu" of La VillasSanta ClaraCuba
  7. 7.Department of Applied Mathematics, Computer Science and StatisticsGhent UniversityGhentBelgium

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-47759-6
  • Copyright Information Springer International Publishing AG 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-47758-9
  • Online ISBN 978-3-319-47759-6
  • Buy this book on publisher's site