Ensemble systems improve the generalization of single classifiers by aggregating the prediction of a set of base classifiers. Assessing classification reliability (posterior probability) is crucial in a number of applications, such as biomedical and diagnosis applications, where the cost of a misclassified input vector can be unacceptable high. Available methods are limited to either calibrate the posterior probability on an aggregated decision value or obtain a posterior probability for each base classifier and aggregate the result. We propose a method that takes advantage of the distribution of the decision values from the base classifiers to summarize a statistic which is subsequently used to generate the posterior probability. Three approaches are considered to fit the probabilistic output to the statistic: the standard Gaussian CDF, isotonic regression, and linear logistic. Even though this study focuses on a bagged support vector machine ensemble (Z-bag), our approach is not limited by the aggregation method selected, the choice of base classifiers, nor the statistic used. Performance is assessed on one artificial and 12 real-world data sets from the UCI Machine Learning Repository. Our approach achieves comparable or better generalization on accuracy and posterior estimation to existing ensemble calibration methods although lowering computational cost.