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Positive-Unlabelled (PU) learning is a growing field of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances, which can be in reality positive or negative, but whose label is unknown.
Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test , volume 1
F. Wilcoxon, S. Katti, and R. Wilcox · 1963
Earlier work this paper cites.
On bayesian methods for seeking the extremum
J. Močkus · 1975
Earlier work this paper cites.
Counting processes and survival analysis
T. Fleming and D. Harrington · 1991
Earlier work this paper cites.
A systematic review of the diagnostic accuracy of physical examination for the detection of cirrhosis
G. De Bruyn and E. Graviss · 2001
Earlier work this paper cites.
Partially supervised classification of text documents
B. Liu, W. S. Lee, P. S. Yu, and X. Li · 2002
Earlier work this paper cites.
Introduction to evolutionary computing , volume 53
A. Eiben and J. Smith · 2003
Earlier work this paper cites.
Statistical comparisons of classifiers over multiple data sets
J. Demšar · 2006
Earlier work this paper cites.
Integrated PET/CT
H. Palmedo, J. Bucerius, A. Joe, et al · 2006
Earlier work this paper cites.
Learning classifiers from only positive and unlabeled data
C. Elkan and K. Noto · 2008
Earlier work this paper cites.
Naive bayes classifier for positive unlabeled learning with uncertainty
J. He, Y. Zhang, X. Li, and Y. Wang · 2010
Earlier work this paper cites.
Distributional similarity vs. PU
X.L. Li, L. Zhang, B. Liu, and S.K. Ng · 2010
Earlier work this paper cites.
Open access series of imaging studies: longitudinal mri data in nondemented and demented older adults
D. Marcus, A. Fotenos, J. Csernansky, J. Morris, and R. Buckner · 2010
Earlier work this paper cites.
Evaluating Learning Algorithms: A Classification Perspective
N. Japkowicz and M. Shah · 2011
Earlier work this paper cites.
Surrogate-assisted evolutionary computation: Recent advances and future challenges
Yaochu Jin · 2011
Earlier work this paper cites.
Building high-performance classifiers using positive and unlabelled examples for text
T. Ke, B. Yang, L. Zhen, et al · 2012
Earlier work this paper cites.
Positive-unlabelled learning for disease gene identification
P. Yang, X. Li, K. Mei, et al · 2012
Earlier work this paper cites.
Instance selection and instance weighting for cross-domain sentiment classification via PU
R. Xia, X. Hu, J. Lu, et al · 2013
Cited alongside, same era.
Clustering-based method for positive and unlabelled text categorization enhanced by improved TFIDF
L. Liu and T. Peng · 2014
Cited alongside, same era.
Diagnostic accuracy of CT
E. Nerad, M.J. Lahaye, M. Maas, et al · 2016
Cited alongside, same era.
Evaluation of a tree-based pipeline optimization tool for automating data science
R. Olson, N. Bartley, R. Urbanowicz, and J. Moore · 2016
Cited alongside, same era.
The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes
B. Pereira, S. Chin, O. Rueda, H. Vollan, E. Provenzano, H. Bardwell, M. Pugh, L. Jones, R. Russell, S. Sammut, et al · 2016
Cited alongside, same era.
Density estimators for positive-unlabeled learning
Taking human out of learning applications: A survey on automated machine learning
Q. Yao, M. Wang, Y. Chen, W. Dai, Y. Li, W. Tu, Q. Yang, and Y. Yu · 2018
Later among the works it cites.
Boosting positive and unlabeled learning for anomaly detection with multi-features
J. Zhang, Z. Wang, J. Meng, et al · 2018
Later among the works it cites.
Positive and unlabeled learning algorithms and applications: A survey
K. Jaskie and A. Spanias · 2019
Later among the works it cites.
Deep forest
Z. Zhou and J. Feng · 2019
Later among the works it cites.
Learning from positive and unlabeled data: A survey
J. Bekker and J. Davis · 2020
Later among the works it cites.
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
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T. Basile, N. Mauro, F. Esposito, S. Ferilli, and A. Vergari · 2017
Cited alongside, same era.
Automated analysis of connected speech reveals early biomarkers of parkinson’s disease in patients with rapid eye movement sleep behaviour disorder
J. Hlavnička, R. Čmejla, T. Tykalová, K. Šonka, E. Růžička, and J. Rusz · 2017
Cited alongside, same era.
The diagnostic accuracy of a single CEA
B. Shinkins, B.D. Nicholson, J. Primrose, et al · 2017
Cited alongside, same era.
Meta-analysis of diagnostic accuracy of magnetic resonance imaging and mammography for breast cancer
Y. Zhang and H. Ren · 2017
Cited alongside, same era.
Poster: A PU
Y. Zhang, L. Li, J. Zhou, et al · 2017
Cited alongside, same era.
Importance of low diagnostic accuracy for early parkinson’s disease
T. Beach and C. Adler · 2018
Cited alongside, same era.
A tutorial on bayesian optimization
P. Frazier · 2018
Cited alongside, same era.
D. Chicco and G. Jurman · 2020
Later among the works it cites.
Performance analysis of machine learning approaches in stroke prediction
M. Emon, M. Keya, T. Meghla, M. Rahman, M. Al Mamun, and M. Kaiser · 2020
Later among the works it cites.
Likelihood prediction of diabetes at early stage using data mining techniques
M. Islam, R. Ferdousi, S. Rahman, and H. Bushra · 2020
Later among the works it cites.
A generative semi-supervised classifier for datasets with unknown classes
S. Schrunner, B.C. Geiger, A. Zernig, and R. Kern · 2020
Later among the works it cites.
Predicting disease-associated circular RNAs
X. Zeng, Y. Zhong, W. Lin, and Q. Zou · 2020
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Greed is good: Exploration and exploitation trade-offs in bayesian optimization
G. De Ath, R. Everson, A. Rahat, and J. Fieldsend · 2021
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A new dictionary-based positive and unlabeled learning method
B. Liu, Z. Liu, and Y. Xiao · 2021
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Benchmark and survey of automated machine learning frameworks
M. Zöller and M. Huber · 2021
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Positive Unlabeled Learning , volume 16
K. Jaskie and A. Spanias · 2022
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A novel observation points‐based positive‐unlabeled learning algorithm
Y. He, X. Li, M. Zhang, et al · 2023
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