Title:
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Learning from unequally reliable blind ensembles of classifiers
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Author:
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Traganitis, Panagiotis; Pagès Zamora, Alba Maria; Giannakis, Georgios B.
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions |
Abstract:
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Abstract:
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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. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Artificial intelligence -- Educational applications -Ensemble learning -Multi-class classification -Unsupervised -Intel·ligència artificial -- Aplicacions a l'educació |
Rights:
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Document type:
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Article - Submitted version Conference Object |
Published by:
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Institute of Electrical and Electronics Engineers (IEEE)
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