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Recent Advances in Ensembles for Feature Selection

  • Verónica Bolón-Canedo
  • Amparo Alonso-Betanzos
Book

Part of the Intelligent Systems Reference Library book series (ISRL, volume 147)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Verónica Bolón-Canedo, Amparo Alonso-Betanzos
    Pages 1-11
  3. Verónica Bolón-Canedo, Amparo Alonso-Betanzos
    Pages 13-37
  4. Verónica Bolón-Canedo, Amparo Alonso-Betanzos
    Pages 39-51
  5. Verónica Bolón-Canedo, Amparo Alonso-Betanzos
    Pages 53-81
  6. Verónica Bolón-Canedo, Amparo Alonso-Betanzos
    Pages 83-96
  7. Verónica Bolón-Canedo, Amparo Alonso-Betanzos
    Pages 97-113
  8. Verónica Bolón-Canedo, Amparo Alonso-Betanzos
    Pages 115-138
  9. Verónica Bolón-Canedo, Amparo Alonso-Betanzos
    Pages 139-156
  10. Verónica Bolón-Canedo, Amparo Alonso-Betanzos
    Pages 157-171
  11. Verónica Bolón-Canedo, Amparo Alonso-Betanzos
    Pages 173-205

About this book

Introduction

This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance.

With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative.

The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges that researchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining. 

Keywords

Ensemble Learning Information Fusion Machine Learning Pattern Recognition Data Reduction Dimensionality Reduction

Authors and affiliations

  • Verónica Bolón-Canedo
    • 1
  • Amparo Alonso-Betanzos
    • 2
  1. 1.Facultad de InformáticaUniversidade da CoruñaA CoruñaSpain
  2. 2.Facultad de InformáticaUniversidade da CoruñaA CoruñaSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-90080-3
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-90079-7
  • Online ISBN 978-3-319-90080-3
  • Series Print ISSN 1868-4394
  • Series Online ISSN 1868-4408
  • Buy this book on publisher's site
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