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Swarm Intelligence for Multi-objective Problems in Data Mining

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Cover of 'Swarm Intelligence for Multi-objective Problems in Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 An Introduction to Swarm Intelligence for Multi-objective Problems
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    Chapter 2 Multi-Criteria Ant Feature Selection Using Fuzzy Classifiers
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    Chapter 3 Multiobjective Particle Swarm Optimization in Classification-Rule Learning
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    Chapter 4 Using Multi-Objective Particle Swarm Optimization for Designing Novel Classifiers
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    Chapter 5 Optimizing Decision Trees Using Multi-objective Particle Swarm Optimization
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    Chapter 6 A Discrete Particle Swarm for Multi-objective Problems in Polynomial Neural Networks used for Classification: A Data Mining Perspective
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    Chapter 8 Mining Rules: A Parallel Multiobjective Particle Swarm Optimization Approach
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    Chapter 9 The Basic Principles of Metric Indexing
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    Chapter 10 Particle Evolutionary Swarm Multi-Objective Optimization for Vehicle Routing Problem with Time Windows
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    Chapter 11 Combining Correlated Data from Multiple Classifiers
Attention for Chapter 6: A Discrete Particle Swarm for Multi-objective Problems in Polynomial Neural Networks used for Classification: A Data Mining Perspective
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Chapter title
A Discrete Particle Swarm for Multi-objective Problems in Polynomial Neural Networks used for Classification: A Data Mining Perspective
Chapter number 6
Book title
Swarm Intelligence for Multi-objective Problems in Data Mining
Published by
Springer, Berlin, Heidelberg, January 2009
DOI 10.1007/978-3-642-03625-5_6
Book ISBNs
978-3-64-203624-8, 978-3-64-203625-5
Authors

Satchidananda Dehuri, Carlos A. Coello Coello, Sung-Bae Cho, Ashish Ghosh

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 33%
Researcher 1 17%
Student > Ph. D. Student 1 17%
Unknown 2 33%
Readers by discipline Count As %
Computer Science 4 67%
Unknown 2 33%

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