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Multiple Classifier Systems

Overview of attention for book
Cover of 'Multiple Classifier Systems'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Ensemble Methods for Tracking and Segmentation (Abstract)
  3. Altmetric Badge
    Chapter 2 Ensembles and Multiple Classifiers: A Game-Theoretic View
  4. Altmetric Badge
    Chapter 3 Anomaly Detection Using Ensembles
  5. Altmetric Badge
    Chapter 4 Learning to Rank with Nonlinear Monotonic Ensemble
  6. Altmetric Badge
    Chapter 5 A Bayesian Approach for Combining Ensembles of GP Classifiers
  7. Altmetric Badge
    Chapter 6 Multiple Classifiers for Graph of Words Embedding
  8. Altmetric Badge
    Chapter 7 A Dynamic Logistic Multiple Classifier System for Online Classification
  9. Altmetric Badge
    Chapter 8 Ensemble Methods for Reinforcement Learning with Function Approximation
  10. Altmetric Badge
    Chapter 9 GRASP Forest: A New Ensemble Method for Trees
  11. Altmetric Badge
    Chapter 10 Ensembles of Decision Trees for Imbalanced Data
  12. Altmetric Badge
    Chapter 11 Compact Ensemble Trees for Imbalanced Data
  13. Altmetric Badge
    Chapter 12 Multiple Classifier Systems
  14. Altmetric Badge
    Chapter 13 Approximate Convex Hulls Family for One-Class Classification
  15. Altmetric Badge
    Chapter 14 Generalized Augmentation of Multiple Kernels
  16. Altmetric Badge
    Chapter 15 Multiple Classifier Systems
  17. Altmetric Badge
    Chapter 16 Multiple Classifier Systems
  18. Altmetric Badge
    Chapter 17 Multiple Classifier Systems
  19. Altmetric Badge
    Chapter 18 Dynamic Ensemble Selection for Off-Line Signature Verification
  20. Altmetric Badge
    Chapter 19 Classifier Selection Approaches for Multi-label Problems
  21. Altmetric Badge
    Chapter 20 Selection Strategies for pAUC-Based Combination of Dichotomizers
  22. Altmetric Badge
    Chapter 21 Sequential Classifier Combination for Pattern Recognition in Wireless Sensor Networks
  23. Altmetric Badge
    Chapter 22 Multi-class Multi-scale Stacked Sequential Learning
  24. Altmetric Badge
    Chapter 23 Multiple Classifier Systems
  25. Altmetric Badge
    Chapter 24 Two Stage Reject Rule for ECOC Classification Systems
  26. Altmetric Badge
    Chapter 25 Introducing the Separability Matrix for Error Correcting Output Codes Coding
  27. Altmetric Badge
    Chapter 26 Improving Accuracy and Speed of Optimum-Path Forest Classifier Using Combination of Disjoint Training Subsets
  28. Altmetric Badge
    Chapter 27 Analyzing the Relationship between Diversity and Evidential Fusion Accuracy
  29. Altmetric Badge
    Chapter 28 Classification by Cluster Analysis: A New Meta-Learning Based Approach
  30. Altmetric Badge
    Chapter 29 C 3E: A Framework for Combining Ensembles of Classifiers and Clusterers
  31. Altmetric Badge
    Chapter 30 A Latent Variable Pairwise Classification Model of a Clustering Ensemble
  32. Altmetric Badge
    Chapter 31 CLOOSTING: CLustering Data with bOOSTING
  33. Altmetric Badge
    Chapter 32 A Geometric Approach to Face Detector Combining
  34. Altmetric Badge
    Chapter 33 Increase the Security of Multibiometric Systems by Incorporating a Spoofing Detection Algorithm in the Fusion Mechanism
  35. Altmetric Badge
    Chapter 34 Cohort Based Approach to Multiexpert Class Verification
  36. Altmetric Badge
    Chapter 35 A Modular Architecture for the Analysis of HTTP Payloads Based on Multiple Classifiers
  37. Altmetric Badge
    Chapter 36 Incremental Boolean Combination of Classifiers
  38. Altmetric Badge
    Chapter 37 Bagging Classifiers for Fighting Poisoning Attacks in Adversarial Classification Tasks
  39. Altmetric Badge
    Chapter 38 Using a Behaviour Knowledge Space Approach for Detecting Unknown IP Traffic Flows
Attention for Chapter 9: GRASP Forest: A New Ensemble Method for Trees
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Chapter title
GRASP Forest: A New Ensemble Method for Trees
Chapter number 9
Book title
Multiple Classifier Systems
Published in
Lecture notes in computer science, January 2011
DOI 10.1007/978-3-642-21557-5_9
Book ISBNs
978-3-64-221556-8, 978-3-64-221557-5
Authors

José F. Diez-Pastor, César García-Osorio, Juan J. Rodríguez, Andrés Bustillo

Editors

Carlo Sansone, Josef Kittler, Fabio Roli

View on publisher site
X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Iran, Islamic Republic of 1 11%
Unknown 8 89%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 33%
Student > Ph. D. Student 2 22%
Other 1 11%
Lecturer 1 11%
Researcher 1 11%
Other 1 11%
Readers by discipline Count As %
Computer Science 5 56%
Chemical Engineering 1 11%
Chemistry 1 11%
Unknown 2 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 October 2011.
All research outputs
#18,297,449
of 22,653,392 outputs
Outputs from Lecture notes in computer science
#6,005
of 8,122 outputs
Outputs of similar age
#159,894
of 180,249 outputs
Outputs of similar age from Lecture notes in computer science
#229
of 318 outputs
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So far Altmetric has tracked 8,122 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 14th percentile – i.e., 14% of its peers scored the same or lower than it.
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We're also able to compare this research output to 318 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.

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