The fusion of different information sources is a persistent and intriguing issue. It
has been addressed for centuries in various disciplines, including political science,
probability and statistics, system reliability assessment, computer science, and
distributed detection in communications. Early seminal work on fusion was carried
out by pioneers such as Laplace and von Neumann. More recently, research
activities in information fusion have focused on pattern recognition. During the
1990s, classifier fusion schemes, especially at the so-called decision-level, emerged
under a plethora of different names in various scientific communities, including
machine learning, neural networks, pattern recognition, and statistics. The different
nomenclatures introduced by these communities reflected their different
perspectives and cultural backgrounds as well as the absence of common forums
and the poor dissemination of the most important results.