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Feature Extraction, Construction and Selection

A Data Mining Perspective

  • Book
  • © 1998

Overview

Editors:
  1. Huan Liu

    1. National University of Singapore, Singapore

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    You can also search for this editor in PubMed   Google Scholar

  2. Hiroshi Motoda

    1. Osaka University, Osaka, Japan

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 453)

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Table of contents (24 chapters)

  1. Front Matter

    Pages i-xxiv
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  2. Background and Foundation

    1. Front Matter

      Pages 1-1
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    2. Less Is More

      • Huan Liu, Hiroshi Motoda
      Pages 3-12

    3. Feature Weighting for Lazy Learning Algorithms

      • David W. Aha
      Pages 13-32

    4. The Wrapper Approach

      • Ron Kohavi, George H. John
      Pages 33-50

    5. Data-Driven Constructive Induction: Methodology and Applications

      • Eric Bloedorn, Ryszard S. Michalski
      Pages 51-68

  3. Subset Selection

    1. Front Matter

      Pages 69-69
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    2. Selecting Features by Vertical Compactness of Data

      • Ke Wang, Suman Sundaresh
      Pages 71-84

    3. Relevance Approach to Feature Subset Selection

      • Hui Wang, David Bell, Fionn Murtagh
      Pages 85-99

    4. Novel Methods for Feature Subset Selection with Respect to Problem Knowledge

      • Pavel Pudil, Jana Novovičová
      Pages 101-116

    5. Feature Subset Selection Using a Genetic Algorithm

      • Jihoon Yang, Vasant Honavar
      Pages 117-136

    6. A Relevancy Filter for Constructive Induction

      • Nada Lavrač, Dragan Gamberger, Peter Turney
      Pages 137-154

  4. Feature Extraction

    1. Front Matter

      Pages 155-155
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    2. Lexical Contextual Relations for the Unsupervised Discovery of Texts Features

      • Patrick Perrin, Fred Petry
      Pages 157-173

    3. Integrated Feature Extraction Using Adaptive Wavelets

      • Yvette Mallet, Olivier de Vel, Danny Coomans
      Pages 175-189

    4. Feature extraction via Neural networks

      • Rudy Setiono, Huan Liu
      Pages 191-204

    5. Using Lattice-Based Framework as a Tool for Feature Extraction

      • Engelbert Mephu Nguifo, Patrick Njiwoua
      Pages 205-218

    6. Constructive Function Approximation

      • Paul E. Utgoff, Doina Precup
      Pages 219-235

  5. Feature Construction

    1. Front Matter

      Pages 237-237
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    2. A Comparison of Constructing Different Types of New Feature For Decision Tree Learning

      • Zijian Zheng
      Pages 239-255
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Keywords

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.

Editors and Affiliations

  • National University of Singapore, Singapore

    Huan Liu

  • Osaka University, Osaka, Japan

    Hiroshi Motoda

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