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. Cappé'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.

中文翻译：

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具有相关代理的基于EM的在线集成分类

一种二元整体分类方法 依序提出了在存在参数不确定性的情况下处理从多个决策代理收集的数据的方法。假定主体形成了相关的群体，其决策被建模为多元伯努利随机向量。二元假设的先验概率和每个假设下结果的相应概率被视为未知的确定性参数。Cappé的在线Expectation-Maximization算法用于估计参数值，然后将其输入到集成分类器中。所提出的技术显示了降低的计算复杂性，同时提供了接近其离线同类产品的性能，这需要多次传递数据。给出了数值示例，以证实结果。