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Feature Selection for Data and Pattern Recognition

  • Book
  • © 2015

Overview

Editors:
  1. Urszula Stańczyk

    1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

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  2. Lakhmi C. Jain

    1. Mawson Lakes Campus, Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia, and University of South Australia, Adelaide, Australia

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Recent research trends in feature selection for data and pattern recognition
  • Points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies
  • Presents approaches in feature selection for data and pattern classification using computational intelligence paradigms
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI, volume 584)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Feature Selection for Data and Pattern Recognition: An Introduction

    • Urszula Stańczyk, Lakhmi C. Jain
    Pages 1-7

  3. Estimation of Feature Importance

    1. Front Matter

      Pages 9-9
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    2. All Relevant Feature Selection Methods and Applications

      • Witold R. Rudnicki, Mariusz Wrzesień, Wiesław Paja
      Pages 11-28

    3. Feature Evaluation by Filter, Wrapper, and Embedded Approaches

      • Urszula Stańczyk
      Pages 29-44

    4. A Geometric Approach to Feature Ranking Based Upon Results of Effective Decision Boundary Feature Matrix

      • Claudia Diamantini, Alberto Gemelli, Domenico Potena
      Pages 45-69

    5. Weighting of Features by Sequential Selection

      • Urszula Stańczyk
      Pages 71-90

  4. Rough Set Approach to Attribute Reduction

    1. Front Matter

      Pages 91-91
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    2. Dependency Analysis and Attribute Reduction in the Probabilistic Approach to Rough Sets

      • Wojciech Ziarko
      Pages 93-111

    3. Structure-Based Attribute Reduction: A Rough Set Approach

      • Yoshifumi Kusunoki, Masahiro Inuiguchi
      Pages 113-160

  5. Rule Discovery and Evaluation

    1. Front Matter

      Pages 161-161
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    2. A Comparison of Rule Induction Using Feature Selection and the LEM2 Algorithm

      • Jerzy W. Grzymała-Busse
      Pages 163-176

    3. Meta-actions as a Tool for Action Rules Evaluation

      • Hakim Touati, Zbigniew W. Raś, James Studnicki
      Pages 177-197

    4. Irrelevant Feature and Rule Removal for Structural Associative Classification Using Structure-Preserving Flat Representation

      • Izwan Nizal Mohd Shaharanee, Fedja Hadzic
      Pages 199-228

  6. Data- and Domain-Oriented Methodologies

    1. Front Matter

      Pages 229-229
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    2. Hubness-Aware Classification, Instance Selection and Feature Construction: Survey and Extensions to Time-Series

      • Nenad Tomašev, Krisztian Buza, Kristóf Marussy, Piroska B. Kis
      Pages 231-262

    3. Selection of Visual Descriptors for the Purpose of Multi-camera Object Re-identification

      • Piotr Dalka, Damian Ellwart, Grzegorz Szwoch, Karol Lisowski, Piotr Szczuko, Andrzej Czyżewski
      Pages 263-303

    4. Improving the Recognition Performance of Moment Features by Selection

      • George A. Papakostas
      Pages 305-327

    5. Signature Selection for Grouped Features with a Case Study on Exon Microarrays

      • Sangkyun Lee
      Pages 329-349

  7. Back Matter

    Pages 351-355
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Keywords

About this book

This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition.

Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks.

This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.

Reviews

“The content of the book is outstanding from the point of view of the novelty of the exposed methods, the clarity of the discourse, and the variety of the illustrative examples. … The book is aimed at researchers and practitioners in the domains of machine learning, computer science, data mining, statistical pattern recognition, and bioinformatics.” (L. State, Computing Reviews, June, 2015)

Editors and Affiliations

  • Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Urszula Stańczyk

  • Mawson Lakes Campus, Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia, and University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

Bibliographic Information

  • Book Title: Feature Selection for Data and Pattern Recognition

  • Editors: Urszula Stańczyk, Lakhmi C. Jain

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-662-45620-0

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2015

  • Hardcover ISBN: 978-3-662-45619-4Published: 15 January 2015

  • Softcover ISBN: 978-3-662-50845-9Published: 24 September 2016

  • eBook ISBN: 978-3-662-45620-0Published: 30 December 2014

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XVIII, 355

  • Number of Illustrations: 54 b/w illustrations, 20 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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