Blind Multiclass Ensemble Classification
This addresses the challenge of ensemble learning for pattern recognition and data analytics, but it appears incremental as it builds on existing ensemble methods with a blind approach.
The paper tackled the problem of combining multiple classifiers without access to their training labels, using a moment matching method with joint tensor and matrix factorization, and achieved performance evaluated on synthetic and real datasets.
The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its strengths and limitations, one is motivated to judiciously fuse multiple algorithms in order to find the "best" performing one, for a given dataset. Ensemble learning aims at such high-performance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a blind scheme for learning from ensembles of classifiers, using a moment matching method that leverages joint tensor and matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. A rigorous performance analysis is derived and the proposed scheme is evaluated on synthetic and real datasets.