Mean-Variance Plots for Ensemble Voting Schemes
by
Pamela J. Davy
University of Wollongong
Coauthors: Virginia L. Wheway (University of Wollongong)

Boosting, bagging and other ensemble classifiers are all based on the idea of fitting multiple classification rules and then applying a voting scheme to determine a consensus. In some methods, the individual classifiers are trained sequentially, so that the occurrence of misclassification errors in earlier iterations can be used to adapt later iterations. It is therefore of interest to consider the sequences of correct and incorrect classifications for individual observations. The edge, or in other words the voting weight assigned to incorrect classes, can be evaluated for each observation after each iteration. A scatter plot based on the mean and variance of the edge over all iterations turns out to be useful diagnostic tool.

Date received: November 14, 2001