Feature Extraction, Construction and Selection

A Data Mining Perspective

  • Huan Liu
  • Hiroshi Motoda

Table of contents

  1. Front Matter
    Pages i-xxiv
  2. Background and Foundation

    1. Front Matter
      Pages 1-1
    2. Huan Liu, Hiroshi Motoda
      Pages 3-12
    3. Ron Kohavi, George H. John
      Pages 33-50
    4. Eric Bloedorn, Ryszard S. Michalski
      Pages 51-68
  3. Subset Selection

    1. Front Matter
      Pages 69-69
    2. Ke Wang, Suman Sundaresh
      Pages 71-84
    3. Hui Wang, David Bell, Fionn Murtagh
      Pages 85-99
    4. Jihoon Yang, Vasant Honavar
      Pages 117-136
    5. Nada Lavrač, Dragan Gamberger, Peter Turney
      Pages 137-154
  4. Feature Extraction

    1. Front Matter
      Pages 155-155
    2. Yvette Mallet, Olivier de Vel, Danny Coomans
      Pages 175-189
    3. Rudy Setiono, Huan Liu
      Pages 191-204
    4. Engelbert Mephu Nguifo, Patrick Njiwoua
      Pages 205-218
    5. Paul E. Utgoff, Doina Precup
      Pages 219-235
  5. Feature Construction

    1. Front Matter
      Pages 237-237
    2. Steve Donoho, Larry Rendell
      Pages 273-288
    3. João Gama, Pavel Brazdil
      Pages 289-303
  6. Combined Approaches

    1. Front Matter
      Pages 305-305
    2. Haleh Vafaie, Kenneth De Jong
      Pages 307-323
    3. Blaž Zupan, Marko Bohanec, Janez Demšar, Ivan Bratko
      Pages 325-340
  7. Applications of Feature Transformation

    1. Front Matter
      Pages 355-355
    2. Matteo Baldoni, Cristina Baroglio, Davide Cavagnino, Lorenza Saitta
      Pages 357-373
    3. Luis Seabra Lopes, Luis M. Camarinha-Matos
      Pages 375-391
  8. Back Matter
    Pages 407-410

About this book


There is broad interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data preprocessing is an essential step in the knowledge discovery process for real-world applications. This book compiles contributions from many leading and active researchers in this growing field and paints a picture of the state-of-art techniques that can boost the capabilities of many existing data mining tools. The objective of this collection is to increase the awareness of the data mining community about the research of feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of our endeavor to produce a contemporary overview of modern solutions, to create synergy among these seemingly different branches, and to pave the way for developing meta-systems and novel approaches. Even with today's advanced computer technologies, discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Feature extraction, construction and selection are a set of techniques that transform and simplify data so as to make data mining tasks easier. Feature construction and selection can be viewed as two sides of the representation problem.


algorithms data analysis data mining genetic algorithms knowledge discovery learning machine learning robot

Editors and affiliations

  • Huan Liu
    • 1
  • Hiroshi Motoda
    • 2
  1. 1.National University of SingaporeSingapore
  2. 2.Osaka UniversityOsakaJapan

Bibliographic information

  • DOI
  • Copyright Information Kluwer Academic Publishers 1998
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7622-4
  • Online ISBN 978-1-4615-5725-8
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site
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