Learning naive Bayes classifiers from positive and unlabelled examples with uncertainty

DOI10.1080/00207721.2011.627475zbMath1305.93180OpenAlexW2048041872MaRDI QIDQ2935076

Jiazhen He, Yang Zhang, Peng Shi, Xue Li

Publication date: 22 December 2014

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2011.627475

zbMATH Keywords

uncertain datanaive Bayespositive unlabelled learningpositive naive Bayes

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Stochastic systems in control theory (general) (93E03)

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Uses Software

• UCI-ml

Cites Work

• Not so naive Bayes: Aggregating one-dependence estimators
• Learning from positive and unlabeled examples
• Estimating the Support of a High-Dimensional Distribution
• A coevolutionary algorithm for rules discovery in data mining
• Unnamed Item

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