
Chapter 10
Ensembles of Classifiers
The term multiple models or ensemble of classifiers is used to identify a set of
classifiers for which individual decisions are in some way combined (typically
by voting) to classify new examples (Dietterich, 1997). The main idea behind
any multiple model system is based on the observation that different learn-
ing algorithms explore different representation languages, search spaces, and
evaluation functions of the hypothesis. How can we explore these differences?
Is it possible to design a set of classifiers that working together can obtain a
better performance than each individual classifier? Multiple models are also
used ...