Abstract

A new family of plug-in classification techniques has recently been developed in the statistics and machine learning literature. A plug-in classification technique (PICT) is a method that takes a standard classifier (such as LDA or TREES) and plugs it into an algorithm to produce a new classifier. The standard classifier is known as the Base Classifier. These methods often produce large improvements over using a single classifier. In this paper we investigate one of these methods and give some motivation for its success.

Citations