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Feature Extraction, Construction and Selection

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Cover of 'Feature Extraction, Construction and Selection'

Table of Contents

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

Citations

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443 Dimensions

Readers on

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52 Mendeley
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1 CiteULike
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Title
Feature Extraction, Construction and Selection
Published by
Springer US, August 1998
DOI 10.1007/978-1-4615-5725-8
ISBNs
978-0-7923-8196-9, 978-1-4613-7622-4, 978-1-4615-5725-8
Editors

Huan Liu, Motoda, Hiroshi

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 2%
Brazil 1 2%
Unknown 50 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 29%
Student > Ph. D. Student 7 13%
Student > Bachelor 5 10%
Researcher 4 8%
Lecturer 3 6%
Other 2 4%
Unknown 16 31%
Readers by discipline Count As %
Computer Science 19 37%
Engineering 9 17%
Business, Management and Accounting 1 2%
Agricultural and Biological Sciences 1 2%
Mathematics 1 2%
Other 4 8%
Unknown 17 33%