Ensembles in Machine Learning Applications

✍ Scribed by Raymond S. Smith, Terry Windeatt (auth.), Oleg Okun, Giorgio Valentini, Matteo Re (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2011
• Tongue: English
• Leaves: 273
• Series: Studies in Computational Intelligence 373
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages -
Facial Action Unit Recognition Using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classifiers....Pages 1-20
On the Design of Low Redundancy Error-Correcting Output Codes....Pages 21-38
Minimally-Sized Balanced Decomposition Schemes for Multi-class Classification....Pages 39-58
Bias-Variance Analysis of ECOC and Bagging Using Neural Nets....Pages 59-73
Fast-Ensembles of Minimum Redundancy Feature Selection....Pages 75-95
Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers....Pages 97-115
Learning Markov Blankets for Continuous or Discrete Networks via Feature Selection....Pages 117-131
Ensembles of Bayesian Network Classifiers Using Glaucoma Data and Expertise....Pages 133-150
A Novel Ensemble Technique for Protein Subcellular Location Prediction....Pages 151-167
Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests....Pages 169-179
Random Oracles for Regression Ensembles....Pages 181-199
Embedding Random Projections in Regularized Gradient Boosting Machines....Pages 201-216
An Improved Mixture of Experts Model: Divide and Conquer Using Random Prototypes....Pages 217-231
Three Data Partitioning Strategies for Building Local Classifiers....Pages 233-250
Back Matter....Pages -

✦ Subjects

Computational Intelligence; Artificial Intelligence (incl. Robotics)