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Multiple Instance Learning

Foundations and Algorithms

  • Book
  • © 2016

Overview

Authors:
  1. Francisco Herrera

    1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

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  2. Sebastián Ventura

    1. Department of Computer Science, University of Córdoba, Córdoba, Spain

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  3. Rafael Bello

    1. Center of Information Studies, Central University “Marta Abreu” of Las Villas, Santa Clara, Cuba

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  4. Chris Cornelis

    1. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

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  5. Amelia Zafra

    1. Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

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  6. Dánel Sánchez-Tarragó

    1. Central University "Marta Abreu" of La Villas, Santa Clara, Cuba

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  7. Sarah Vluymans

    1. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

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  • Offers a comprehensive overview of multiple instance learning widely used to classify and label texts, pictures, videos and music in the Internet

  • Provides the user with the most relevant algorithms for MIL and the most representative applications

  • Covers both the background and future directions of the field

  • Includes supplementary material: sn.pub/extras

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xi
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  2. Introduction

    • Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 1-16

  3. Multiple Instance Learning

    • Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 17-33

  4. Multi-instance Classification

    • Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 35-66

  5. Instance-Based Classification Methods

    • Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 67-98

  6. Bag-Based Classification Methods

    • Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 99-126

  7. Multi-instance Regression

    • Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 127-140

  8. Unsupervised Multiple Instance Learning

    • Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 141-167

  9. Data Reduction

    • Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 169-189

  10. Imbalanced Multi-instance Data

    • Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 191-208

  11. Multiple Instance Multiple Label Learning

    • Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez-Tarragó et al.
    Pages 209-230

  12. Back Matter

    Pages 231-233
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Keywords

About this book

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.




Authors and Affiliations

  • Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Francisco Herrera

  • Department of Computer Science, University of Córdoba, Córdoba, Spain

    Sebastián Ventura

  • Center of Information Studies, Central University “Marta Abreu” of Las Villas, Santa Clara, Cuba

    Rafael Bello

  • Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Chris Cornelis, Sarah Vluymans

  • Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

    Amelia Zafra

  • Central University "Marta Abreu" of La Villas, Santa Clara, Cuba

    Dánel Sánchez-Tarragó

Bibliographic Information

  • Book Title: Multiple Instance Learning

  • Book Subtitle: Foundations and Algorithms

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

  • DOI: https://doi.org/10.1007/978-3-319-47759-6

  • Publisher: Springer Cham

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

  • Copyright Information: Springer International Publishing AG 2016

  • Hardcover ISBN: 978-3-319-47758-9Published: 17 November 2016

  • Softcover ISBN: 978-3-319-83815-1Published: 29 June 2018

  • eBook ISBN: 978-3-319-47759-6Published: 08 November 2016

  • Edition Number: 1

  • Number of Pages: XI, 233

  • Number of Illustrations: 6 b/w illustrations, 40 illustrations in colour

  • Topics: Artificial Intelligence, Image Processing and Computer Vision, Algorithm Analysis and Problem Complexity

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