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Feature Selection for Data and Pattern Recognition

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Cover of 'Feature Selection for Data and Pattern Recognition'

Table of Contents

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    Book Overview
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    Chapter 1 Feature Selection for Data and Pattern Recognition: An Introduction
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    Chapter 2 All Relevant Feature Selection Methods and Applications
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    Chapter 3 Feature Evaluation by Filter, Wrapper, and Embedded Approaches
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    Chapter 4 A Geometric Approach to Feature Ranking Based Upon Results of Effective Decision Boundary Feature Matrix
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    Chapter 5 Weighting of Features by Sequential Selection
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    Chapter 6 Dependency Analysis and Attribute Reduction in the Probabilistic Approach to Rough Sets
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    Chapter 7 Structure-Based Attribute Reduction: A Rough Set Approach
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    Chapter 8 A Comparison of Rule Induction Using Feature Selection and the LEM2 Algorithm
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    Chapter 9 Meta-actions as a Tool for Action Rules Evaluation
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    Chapter 10 Irrelevant Feature and Rule Removal for Structural Associative Classification Using Structure-Preserving Flat Representation
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    Chapter 11 Hubness-Aware Classification, Instance Selection and Feature Construction: Survey and Extensions to Time-Series
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    Chapter 12 Selection of Visual Descriptors for the Purpose of Multi-camera Object Re-identification
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    Chapter 13 Improving the Recognition Performance of Moment Features by Selection
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    Chapter 14 Signature Selection for Grouped Features with a Case Study on Exon Microarrays
Attention for Chapter 10: Irrelevant Feature and Rule Removal for Structural Associative Classification Using Structure-Preserving Flat Representation
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Citations

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Chapter title
Irrelevant Feature and Rule Removal for Structural Associative Classification Using Structure-Preserving Flat Representation
Chapter number 10
Book title
Feature Selection for Data and Pattern Recognition
Published in
Studies in Computational Intelligence, January 2015
DOI 10.1007/978-3-662-45620-0_10
Book ISBNs
978-3-66-245619-4, 978-3-66-245620-0
Authors

Izwan Nizal Mohd Shaharanee, Fedja Hadzic, Shaharanee, Izwan Nizal Mohd, Hadzic, Fedja

Timeline

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 2%
Unknown 44 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 42%
Student > Master 9 20%
Lecturer 4 9%
Student > Postgraduate 4 9%
Lecturer > Senior Lecturer 3 7%
Other 6 13%
Readers by discipline Count As %
Social Sciences 12 27%
Business, Management and Accounting 11 24%
Computer Science 9 20%
Linguistics 5 11%
Arts and Humanities 3 7%
Other 5 11%