Abstract:
Ensemble learning, which combines multiple models, has performed well in many tasks. Unsupervised ensemble classification is the focus here. Unsupervised means the ensemble combiner does not know the ground-truth labels each classifier was trained on. This work introduces an unsupervised scheme for learning from ensembles of classifiers with data dependencies, unlike most previous unsupervised ensemble classification schemes. Sequential and graphed data dependencies are considered. New moment matching and Expectation-Maximization algorithms are created. On synthetic and real datasets, these algorithms show that the meta-learner’s data dependencies help unsupervised ensemble classification.
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