Bias-variance analysis of ECOC and bagging using neural nets

One of the methods used to evaluate the performance of ensemble classiers is bias and variance analysis. In this paper, we analyse bagging and ECOC ensembles using bias-variance domain of James [1] and make a comparison with single classifiers, when using Neural Networks (NNs) as base classifiers. As the performance of the ensembles depends on the individual base classiers, it is important to understand the overall trends when the parameters of the base classiers, nodes and epochs for NNs, are changed. We show experimentally on 5 artificial and 4 UCI MLR datasets that there are some clear trends in the analysis that should be taken into consideration while designing NN classifier systems.