Applications of Supervised and Unsupervised Ensemble Methods [electronic resource] / edited by Oleg Okun, Giorgio Valentini.

An Ensemble Pruning Primer -- Evade Hard Multiple Classifier Systems -- A Personal Antispam System Based on a Behaviour-Knowledge Space Approach -- Weighted Decoding ECOC for Facial Action Unit Classification -- Prediction of Gene Function Using Ensembles of SVMs and Heterogeneous Data Sources -- Partitioner Trees for Classification: A New Ensemble Method -- Disturbing Neighbors Diversity for Decision Forests -- Improving Supervised Learning with Multiple Clusterings -- The Neighbors Voting Algorithm and Its Applications -- Clustering Ensembles with Active Constraints -- Verifiable Ensembles of Low-Dimensional Submodels for Multi-class Problems with Imbalanced Misclassification Costs -- Independent Data Model Selection for Ensemble Dispersion Forecasting -- Integrating Liknon Feature Selection and Committee Training -- Evaluating Hybrid Ensembles for Intelligent Decision Support for Intensive Care.

This book contains the extended papers presented at the 2nd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) held on 21-22 July, 2008 in Patras, Greece, in conjunction with the 18th European Conference on Artificial Intelligence (ECAI’2008). This workshop was a successor of the smaller event held in 2007 in conjunction with 3rd Iberian Conference on Pattern Recognition and Image Analysis, Girona, Spain. The success of that event as well as the publication of workshop papers in the edited book “Supervised and Unsupervised Ensemble Methods and their Applications”, published by Springer-Verlag in Studies in Computational Intelligence Series in volume 126, encouraged us to continue a good tradition. The purpose of this book is to support practitioners in various branches of science and technology to adopt the ensemble approach for their daily research work. We hope that fourteen chapters composing the book will be a good guide in the sea of numerous opportunities for ensemble methods.