Title: Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles,
Abstract: For many applications, an ensemble of base classifiers is an effective
solution. The tuning of its parameters(number of classes, amount of data on
which each classifier is to be trained on, etc.) requires G, the generalization
error of a given ensemble. The efficient estimation of G is the focus of this
paper. The key idea is to approximate the variance of the class
scores/probabilities of the base classifiers over the randomness imposed by the
training subset by normal/beta distribution at each point x in the input
feature space. We estimate the parameters of the distribution using a small set
of randomly chosen base classifiers and use those parameters to give efficient
estimation schemes for G. We give empirical evidence for the quality of the
various estimators. We also demonstrate their usefulness in making design
choices such as the number of classifiers in the ensemble and the size of a
subset of data used for training that is needed to achieve a certain value of
generalization error. Our approach also has great potential for designing
distributed ensemble classifiers.