Abstract In many Pattern Recognition applications, the achievement of acceptable
recognition rates is conditioned by the large pattern variability, whose distribution cannot be …

Many theoretical and experimental studies have shown that a multiple classi? er system is
an e? ective technique for reducing prediction errors [9, 10, 11, 20, 19]. These studies …

The theory and practice of Multiple Classifier Systems (MCS) and related methods address
the issues surrounding the optimality of decision-making in a multiple classifier framework …

As multiple experts can confront and exchange their ideas in order to improve the decision-
making process, a pattern recognition system can use several classifiers in order to improve …

More than a decade ago, combining multiple classifiers was proposed as a possible solution
to the problems posed by the traditional pattern classification approach which involved …

The belief that a committee of people make better decisions than any individual is widely
held and appreciated. We also understand that, for this to be true, the members of the …

One vigorous branch of research aimed at improving the performance of pattern recognition
systems explores the possibilities for exploiting the differences between a set of variously …

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 …

In this paper we propose a new approach for dynamic selection of ensembles of classifiers.
Based on the concept named multistage organizations, the main objective of which is to …

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 …