Multiclassifier systems, the focus of this article, provide scientists and data professionals with powerful techniques for tackling complex datasets. The basic idea behind the multiclassifier approach is to average the decisions or hypotheses of a diverse group of classifiers in order to produce a better decision or hypothesis.

As an introduction to our subject, we begin with a detailed examination of the canonic single-classifier system, as this provides the mathematical foundation needed in our presentation of multiclassifier systems. We then describe some important methods for constructing multiclassifier systems at all the levels mentioned above: the classifier level, the combination level, the data level, and the feature level.

We thus end our overview of multiclassifier systems with a section that provides guidance for experimentally constructing general-purpose (GP) multiclassifier systems.