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 reasons. 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 v...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Kelvin T. Leung, Douglas Stott Parker Jr.
• Keywords: Data Mining, KDD 2003, Plurality Method, Plurality Voting Method, Specific Voting Methods