A new family of plug-in classification techniques has recently been de
veloped in the statistics and machine learning literature. A plug-in c
lassification technique (PICT) is a method that takes a standard class
ifier (such as LDA or TREES) and plugs it into an algorithm to produce
a new classifier. The standard classifier is known as the base classi
fier. These methods often produce large improvements over using a sing
le classifier. In this article we investigate one of these methods and
give some motivation for its success.