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Home > Computing and Information Technology > Databases > Feature Extraction, Construction and Selection: A Data Mining Perspective(453 The Springer International Series in Engineering and Computer Science)
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Feature Extraction, Construction and Selection: A Data Mining Perspective(453 The Springer International Series in Engineering and Computer Science)

Feature Extraction, Construction and Selection: A Data Mining Perspective(453 The Springer International Series in Engineering and Computer Science)


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About the Book

There is an interest in feature extraction, construction, and selection among practitioners from statistics, pattern recognition, and data mining to machine learning. Data pre-processing is an essential step in the knowledge discovery process for real-world applications. This text compiles contributions from researchers in this field and paints a picture of the 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 research into feature extraction, construction and selection, which are currently conducted mainly in isolation. This book is part of an endeavour 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. The book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems. It can also serve as a reference work for those who are conducting research into feature extraction, construction and selection.

Table of Contents:
1 Less is More.- 2 Feature Weighting for Lazy Learning Algorithms.- 3 The Wrapper Approach.- 4 Data-driven Constructive Induction: Methodology and Applications.- 5 Selecting Features by Vertical Compactness of Data.- 6 Relevance Approach to Feature Subset Selection.- 7 Novel Methods for Feature Subset Selection with Respect to Problem Knowledge.- 8 Feature Subset Selection Using A Genetic Algorithm.- 9 A Relevancy Filter for Constructive Induction.- 10 Lexical Contextual Relations for the Unsupervised Discovery of Texts Features.- 11 Integrated Feature Extraction Using Adaptive Wavelets.- 12 Feature Extraction via Neural Networks.- 13 Using Lattice-based Framework as a Tool for Feature Extraction.- 14 Constructive Function Approximation.- 15 A Comparison of Constructing Different Types of New Feature for Decision Tree Learning.- 16 Constructive Induction: Covering Attribute Spectrum.- 17 Feature Construction Using Fragmentary Knowledge.- 18 Constructive Induction on Continuous Spaces.- 19 Evolutionary Feature Space Transformation.- 20 Feature Transformation by Function Decomposition.- 21 Constructive Induction of Cartesian Product Attributes.- 22 Towards Automatic Fractal Feature Extraction for Image Recognition.- 23 Feature Transformation Strategies for a Robot Learning Problem.- 24 Interactive Genetic Algorithm Based Feature Selection and Its Application to Marketing Data Analysis.


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Product Details
  • ISBN-13: 9780792381969
  • Publisher: Kluwer Academic Publishers
  • Publisher Imprint: Kluwer Academic Publishers
  • Height: 235 mm
  • No of Pages: 410
  • Returnable: Y
  • Sub Title: A Data Mining Perspective
  • ISBN-10: 0792381963
  • Publisher Date: 31 Aug 1998
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Series Title: 453 The Springer International Series in Engineering and Computer Science
  • Width: 155 mm


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Feature Extraction, Construction and Selection: A Data Mining Perspective(453 The Springer International Series in Engineering and Computer Science)
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Feature Extraction, Construction and Selection: A Data Mining Perspective(453 The Springer International Series in Engineering and Computer Science)
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