Feature Extraction, Construction and Selection

1. Front Matter

   Pages i-xxiv

   PDF

2. Background and Foundation

   1. Front Matter

      Pages 1-1

      PDF

   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

      PDF

   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

      PDF

   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

      PDF

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

      • Zijian Zheng

      Pages 239-255

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

• National University of Singapore, Singapore

  Huan Liu

• Osaka University, Osaka, Japan

  Hiroshi Motoda

Policies and ethics