A binary ensemble classification method that sequentially processes the data collected from multiple decision agents in the presence of parameter uncertainties is proposed. Agents are assumed to form correlated groups whose decisions are modeled as multivariate Bernoulli random vectors. The prior probabilities of the binary hypotheses and the corresponding probabilities of the outcomes under each hypothesis are treated as unknown deterministic parameters. Cappe's online Expectation-Maximization algorithm is employed to estimate the parameter values, which are then fed into the ensemble classifier. The proposed technique is shown the reduce the computational complexity while delivering performance close to its offline counterpart, which requires multiple passes over the data. Numerical examples are presented to corroborate the results.