Exploiting unlabeled data to enhance ensemble diversity

DOI10.1007/S10618-011-0243-9zbMath1260.68354arXiv0909.3593OpenAlexW2570107029MaRDI QIDQ1944971

Min-Ling Zhang, Zhi-Hua Zhou

Publication date: 28 March 2013

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0909.3593

zbMATH Keywords

machine learningdiversityensemble learningunlabeled data

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Graph-based sparse Bayesian broad learning system for semi-supervised learning ⋮ RandGA: injecting randomness into parallel genetic algorithm for variable selection

Uses Software

• UCI-ml
• AdaBoost.MH
• NeC4.5
• SemiBoost

Cites Work

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• Bagging predictors
• Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
• TUNING DIVERSITY IN BAGGED ENSEMBLES

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