Learning from unequally reliable blind ensembles of classifiers

The rising interest in pattern recognition and data analytics has spurred the development of a plethora of machine learning algorithms and tools. However, as each algorithm has its strengths and weaknesses, one is motivated to judiciously fuse multiple algorithms in order to find the “best” performing one, for a given dataset. Ensemble learning aims to create a highperformance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a simple blind scheme for learning from ensembles of classifiers, using joint matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. Performance is evaluated on synthetic and real datasets.