Supervised and Unsupervised Ensemble Methods and their Applications

✍ Scribed by Ana Fred, André Lourenço (auth.), Dr. Oleg Okun, Giorgio Valentini (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2008
• Tongue: English
• Leaves: 186
• Series: Studies in Computational Intelligence 126
• Edition: 1
• Category: Library

✦ Synopsis

This book was inspired by the last argument and resulted from the workshop on Supervised and Unsupervised Ensemble Methods and their Applications (briefly, SUEMA) organized on June 4, 2007 in Girona, Spain. This workshop was held in conjunction with the 3rd Iberian Conference on Pattern Recognition and Image Analysis and was intended to encompass the progress in the ensemble applications made by the Iberian and international scholars. Despite its small format, SUEMA attracted researchers from Spain, Portugal, France, USA, Italy, and Finland, who presented interesting ideas about using the ensembles in various practical cases. Encouraged by this enthusiastic reply, we decided to publish workshop papers in an edited book, since CD proceedings were the only media distributed among the workshop participants at that time. The book includes nine chapters divided into two parts, assembling contributions to the applications of supervised and unsupervised ensembles.

The book is intended to be primarily a reference work. It could be a good complement to two excellent books on ensemble methodology – “Combining pattern classifiers: methods and algorithms” by Ludmila Kuncheva (John Wiley & Sons, 2004) and “Decomposition methodology for knowledge discovery and data mining: theory and applications” by Oded Maimon and Lior Rokach (World Scientific, 2005).

✦ Table of Contents

Front Matter....Pages I-XIV
Cluster Ensemble Methods: from Single Clusterings to Combined Solutions....Pages 3-30
Random Subspace Ensembles for Clustering Categorical Data....Pages 31-48
Ensemble Clustering with a Fuzzy Approach....Pages 49-69
Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysis....Pages 71-88
Intrusion Detection in Computer Systems Using Multiple Classifier Systems....Pages 91-113
Ensembles of Nearest Neighbors for Gene Expression Based Cancer Classification....Pages 115-134
Multivariate Time Series Classification via Stacking of Univariate Classifiers....Pages 135-151
Gradient Boosting GARCH and Neural Networks for Time Series Prediction....Pages 153-164
Cascading with VDM and Binary Decision Trees for Nominal Data....Pages 165-178
Erratum....Pages 181-181
Back Matter....Pages 179-180

✦ Subjects

Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)