Ensembles in Machine Learning Applications

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