document: Empirical comparisons of various voting methods in bagging Finding effective methods for developing an ensemble of models has been an active research area of large-scale data mining in recent years . Models learned from data are often subject to some degree of uncertainty , for a variety of resoans . In classification , ensembles of models provide a useful means of averaging out error introduced by individual classifiers , hence reducing the generalization error of prediction . The plurality voting method is often chosen for bagging , because of its simplicity of implementation . However , the plurality approach to model reconciliation is ad-hoc . There are many other voting methods to choose from , including the anti-plurality method , the plurality method with elimination , the Borda count method , and Condorcet 's method of pairwise comparisons . Any of these could lead to a better method for reconciliation . In this paper , we analyze the use of these voting methods in model reconciliation . We present empirical results comparing performance of these voting methods when applied in bagging . These results include some surprises , and among other things suggest that ( 1 ) plurality is not always the best voting method ; ( 2 ) the number of classes can affect the performance of voting methods ; and ( 3 ) the degree of dataset noise can affect the performance of voting methods . While it is premature to make final judgments about specific voting methods , the results of this work raise interesting questions , and they open the door to the application of voting theory in classification theory .
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