Artificial Intelligence: Foundations, Theory, and Algorithms

Feature Selection for High-Dimensional Data

Authors: Bolón-Canedo, Verónica, Sánchez Maroño, Noelia, Alonso-Betanzos, Amparo

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  • Explains how to choose an optimal subset of features according to a certain criterion
  • Coherent, comprehensive approach to feature subset selection in the scope of classification problems
  • Authors explain the "Big Dimensionality" problem
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Hardcover $99.99
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  • ISBN 978-3-319-21857-1
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Softcover $89.99
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  • ISBN 978-3-319-36643-2
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About this book

This book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data.

The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms.

They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers.

The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining.

About the authors

Dr. Verónica Bolón-Canedo received her PhD in Computer Science from the University of A Coruña, where she is currently a postdoctoral researcher. Her research interests include data mining, feature selection and machine learning. 

Dr. Noelia Sánchez-Maroño received her PhD in 2005 from the University of A Coruña, where she is currently a lecturer. Her research interests include agent-based modeling, machine learning and feature selection.

Prof. Amparo Alonso-Betanzos received her PhD in 1988 from the University of Santiago de Compostela, she is a Chair Professor in the Dept. of Computer Science at the University of A Coruña (Spain) and coordinator of the Laboratory for Research and Development in Artificial Intelligence. Her areas of expertise are machine learning, feature selection, knowledge-based systems, and their applications to fields such as predictive maintenance in engineering or predicting gene expression in bioinformatics.

Table of contents (6 chapters)

  • Introduction to High-Dimensionality

    Bolón-Canedo, Verónica (et al.)

    Pages 1-12

  • Foundations of Feature Selection

    Bolón-Canedo, Verónica (et al.)

    Pages 13-28

  • A Critical Review of Feature Selection Methods

    Bolón-Canedo, Verónica (et al.)

    Pages 29-60

  • Feature Selection in DNA Microarray Classification

    Bolón-Canedo, Verónica (et al.)

    Pages 61-94

  • Application of Feature Selection to Real Problems

    Bolón-Canedo, Verónica (et al.)

    Pages 95-124

Buy this book

eBook $69.99
price for USA in USD (gross)
  • ISBN 978-3-319-21858-8
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $99.99
price for USA in USD
  • ISBN 978-3-319-21857-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $89.99
price for USA in USD
  • ISBN 978-3-319-36643-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Feature Selection for High-Dimensional Data
Authors
Series Title
Artificial Intelligence: Foundations, Theory, and Algorithms
Copyright
2015
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-21858-8
DOI
10.1007/978-3-319-21858-8
Hardcover ISBN
978-3-319-21857-1
Softcover ISBN
978-3-319-36643-2
Series ISSN
2365-3051
Edition Number
1
Number of Pages
XV, 147
Topics