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  • © 2015

Feature Selection for High-Dimensional Data

  • 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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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xv
  2. Introduction to High-Dimensionality

    • Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos
    Pages 1-12
  3. Foundations of Feature Selection

    • Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos
    Pages 13-28
  4. A Critical Review of Feature Selection Methods

    • Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos
    Pages 29-60
  5. Feature Selection in DNA Microarray Classification

    • Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos
    Pages 61-94
  6. Application of Feature Selection to Real Problems

    • Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos
    Pages 95-124
  7. Emerging Challenges

    • Verónica Bolón-Canedo, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos
    Pages 125-132
  8. Back Matter

    Pages 133-147

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.

Authors and Affiliations

  • Facultad de Informática, Universidad de A Coruña, A Coruña, Spain

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

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.

Bibliographic Information

Buy it now

Buying options

eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access