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Learning algorithms that aggregate predictions from an ensemble of diversebase classifiers consistently outperform individual methods. Many of thesestrategies have been developed in a supervised setting, where the accuracy ofeach base classifier can be empirically measured and this information isincorporated in the training process. However, the reliance on labeled dataprecludes the application of ensemble methods to many real world problems wherelabeled data has not been curated. To this end we developed a new theoreticalframework for binary classification, the Strategy for Unsupervised MultipleMethod Aggregation (SUMMA), to estimate the performances of base classifiersand an optimal strategy for ensemble learning from unlabeled data.

Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions