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Table of contents Part I Ensembles of Clustering Methods and Their Applications.- Cluster Ensemble Methods: from Single Clusterings to Combined Solutions.- Random Subspace Ensembles for Clustering Categorical Data.- Ensemble Clustering with a Fuzzy Approach.- Collaborative Multi-Strategical Clustering for Object-Oriented Image Analysis.- Part II Ensembles of Classification Methods and Their Applications Intrusion Detection in Computer Systems Using Multiple Classifier Systems.- Ensembles of Nearest Neighbors for Gene Expression Based Cancer Classification.- Multivariate Time Series Classification via Stacking of Univariate Classifiers.- Gradient Boosting GARCH and Neural Networks for Time Series Prediction.- Cascading with VDM and Binary Decision Trees for Nominal Data.
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.
Recently, bias-variance decomposition of error has been used as a tool to study the behavior of learning algorithms and to develop new ensemble methods well suited to the bias-variance characteristics of base learners. We propose methods and procedures, based on Domingo's unified bias-variance theory, to evaluate and quantitatively measure the bias-variance decomposition of error in ensembles of learning machines. We apply these methods to study and compare the bias-variance characteristics of single support vector machines (SVMs) and ensembles of SVMs based on resampling techniques, and their relationships with the cardinality of the training samples. In particular, we present an experimental bias-variance analysis of bagged and random aggregated ensembles of SVMs in order to verify their theoretical variance reduction properties. The...
Summary: We present a new R package for the assessment of the reliability of clusters discovered in high dimensional DNA microarray data. The package implements methods based on random projections that approximately preserve distances between examples in the projected subspaces. Availability: http://homes.dsi.unimi.it/~valenti/SW/clusterv/download/clusterv_1.0.tar.gz. Supplementary Information: http://homes.dsi.unimi.it/~valenti/SW/clusterv.
The assessment of the reliability of clusters discovered in bio-molecular data is a central issue in several bioinformatics problems, ranging from the definition of new taxonomies of malignancies based on bio-molecular data, to the validation of clusters of co-regulated or co-expressed genes, or the discovery of functional relationships from protein-protein interaction data. Recently, several methods based on the concept of stability have been proposed to estimate the reliability and the "optimal" number of clusters. In this conceptual framework multiple clusterings are obtained by introducing perturbations into the original data, and a clustering is considered reliable if it is approximately maintained across multiple perturbations. Different procedures have been introduced to randomly perturb the data, ranging from bootstrapping tech...
The R package mosclust (model order selection for clustering problems) implements algorithms based on the concept of stability for discovering significant structures in bio-molecular data. The software library provides stability indices obtained through different data perturbations methods (resampling, random projections, noise injection), as well as statistical tests to assess the significance of multi-level structures singled out from the data.
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