A current focus of intense research in pattern classification is the combination of several
classifier systems, which can be built following either the same or different models and/or …

A number of classifier fusion methods have been recently developed opening an alternative
approach leading to a potential improvement in the classification performance. As there is …

An intense research around classifier fusion in recent years revealed that combining
performance strongly depends on careful selection of classifiers to be combined. Classifier …

This book delivers a definite and compact knowledge on how hybridization can help
improving the quality of computer classification systems. In order to make readers clearly …

For any pattern classification task, an increase in data size, number of classes, dimension of
the feature space, and interclass separability affect the performance of any classifier. A …

Individual classification models are recently challenged by combined pattern recognition
systems, which often show better performance. In such systems the optimal set of classifiers …

Much work has been done in the past decade to combine decisions of multiple classifiers in
order to obtain improved recognition results. Many methodologies have been designed and …

There are two main paradigms in combining different classification algorithms: Classifier
Selection and Classifier Fusion. The first one selects a single model for classifying a new …

Multi-Classifier Systems (MCSs) have fast been gaining popularity among researchers for
their ability to fuse together multiple classification outputs for better accuracy and …

These proceedings are a record of the Multiple Classifier Systems Workshop, MCS 2009,
held at the University of Iceland, Reykjavik, Iceland in June 2009. Being the eighth in a well …