Learning from unequally reliable blind ensembles of classifiers
Tipo de documentoTexto en actas de congreso
Fecha de publicación2017
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condiciones de accesoAcceso abierto
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 high- performance 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.
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CitaciónTraganitis, P. A., Pagès-Zamora, A., Giannakis, G.B. Learning from unequally reliable blind ensembles of classifiers. A: IEEE Global Conference on Signal and Information Processingg. "2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017): Montreal, Quebec, Canada: 14-16 November 2017". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 106-110.
Versión del editorhttps://ieeexplore.ieee.org/document/8308613/