Ensembles in Machine Learning Applications

1. Front Matter

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2. Facial Action Unit Recognition Using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classifiers

   • Raymond S. Smith, Terry Windeatt

   Pages 1-20

3. On the Design of Low Redundancy Error-Correcting Output Codes

   • Miguel Ángel Bautista, Sergio Escalera, Xavier Baró, Oriol Pujol, Jordi Vitrià, Petia Radeva

   Pages 21-38

4. Minimally-Sized Balanced Decomposition Schemes for Multi-class Classification

   • Evgueni N. Smirnov, Matthijs Moed, Georgi Nalbantov, Ida Sprinkhuizen-Kuyper

   Pages 39-58

5. Bias-Variance Analysis of ECOC and Bagging Using Neural Nets

   • Cemre Zor, Terry Windeatt, Berrin Yanikoglu

   Pages 59-73

6. Fast-Ensembles of Minimum Redundancy Feature Selection

   • Benjamin Schowe, Katharina Morik

   Pages 75-95

7. Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers

   • Rakkrit Duangsoithong, Terry Windeatt

   Pages 97-115

8. Learning Markov Blankets for Continuous or Discrete Networks via Feature Selection

   • Houtao Deng, Saylisse Davila, George Runger, Eugene Tuv

   Pages 117-131

9. Ensembles of Bayesian Network Classifiers Using Glaucoma Data and Expertise

   • Stefano Ceccon, David Garway-Heath, David Crabb, Allan Tucker

   Pages 133-150

10. A Novel Ensemble Technique for Protein Subcellular Location Prediction

    • Alessandro Rozza, Gabriele Lombardi, Matteo Re, Elena Casiraghi, Giorgio Valentini, Paola Campadelli

    Pages 151-167

11. Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests

    • Haytham Elghazel, Alex Aussem, Florence Perraud

    Pages 169-179

12. Random Oracles for Regression Ensembles

    • Carlos Pardo, Juan J. Rodríguez, José F. Díez-Pastor, César García-Osorio

    Pages 181-199

13. Embedding Random Projections in Regularized Gradient Boosting Machines

    • Pierluigi Casale, Oriol Pujol, Petia Radeva

    Pages 201-216

14. An Improved Mixture of Experts Model: Divide and Conquer Using Random Prototypes

    • Giuliano Armano, Nima Hatami

    Pages 217-231

15. Three Data Partitioning Strategies for Building Local Classifiers

    • Indrė Žliobaitė

    Pages 233-250

16. Back Matter

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This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods
and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain).
As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms – advanced machine
learning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a group
of algorithms, each of which first independently solves the task at hand by assigning a class or cluster label
(voting) to instances in a dataset and after that all votes are combined together to produce the final class or
cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems.
 
This book consists of 14 chapters, each of which can be read independently of the others. In addition to two
previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or
programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in
practice and to help to both researchers and engineers developing ensemble applications.

• Computational Intelligence
• Computational Intelligence
• Ensembles in Machine Learning Applications
• Ensembles in Machine Learning Applications
• Machine Learning
• Machine Learning

• University of Malmo, Malmö, Sweden

  Oleg Okun

• Department of Computer Science, University of Milan, Milano, Italy

  Giorgio Valentini

• Department of Computer Science, University of Milan, Milano, Italia

  Matteo Re

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