  Learning algorithms that aggregate predictions from an ensemble of diverse
base classifiers consistently outperform individual methods. Many of these
strategies have been developed in a supervised setting, where the accuracy of
each base classifier can be empirically measured and this information is
incorporated in the training process. However, the reliance on labeled data
precludes the application of ensemble methods to many real world problems where
labeled data has not been curated. To this end we developed a new theoretical
framework for binary classification, the Strategy for Unsupervised Multiple
Method Aggregation (SUMMA), to estimate the performances of base classifiers
and an optimal strategy for ensemble learning from unlabeled data.
