Ensemble diversity measures and their application to thinning

The diversity of an ensemble can be calculated in a variety of ways. Here a diversity metric and a means for altering the diversity of an ensemble, called “thinning”, are introduced. We experiment with thinning algorithms evaluated on ensembles created by several techniques on 22 publicly available datasets. When compared to other methods, our percentage correct diversity measure algorithm shows a greater correlation between the increase in voted ensemble accuracy and the diversity value. Also, the analysis of different ensemble creation methods indicates each has varying levels of diversity. Finally, the methods proposed for thinning again show that ensembles can be made smaller without loss in accuracy. Information Fusion Journal