Combining multiple non-homogeneous classifiers: an empirical approach

Abstract

The use of prior behavior of a classifier, as measured by the confusion matrix, can yield useful information for merging multiple classifiers. In particular a ranking of possible classes can be produced which can allow Borda type reconciliation methods to be applied. A simulation of multiple classifiers is used to evaluate this idea, and compare with three other classifier combination techniques.