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The problem of developing binary classifiers from positive and unlabeled data is often encountered in machine learning.
On the identifiability of finite mixtures
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Maximum likelihood from data via the EM algorithm
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Sample selection bias as a specification error
J. Heckman · 1979
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On optimal and data-based histograms
D. W. Scott · 1979
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Identifiability of mixtures
G. M. Tallis and P. Chesson · 1982
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Classification and regression trees
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J. Park and I. W. Sandberg · 1991
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Classification via kernel product estimators
C. A. Cooley and S. N. MacEachern · 1998
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PAC learning from positive statistical queries
F. Denis · 1998
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Probabilistic outputs for support vector machines and comparison to regularized likelihood methods , pages 61–74
J. C. Platt · 1999
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Finite mixture models
G. J. McLachlan and D. Peel · 2000
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The foundations of cost-sensitive learning
C. Elkan · 2001
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The elements of statistical learning: data mining, inference, and prediction
T. Hastie, R. Tibshirani, and J. H. Friedman · 2001
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Adjusting the outputs of a classifier to new a priori probabilities may significantly improve classification accuracy: evidence from a multi-class problem in remote sensing
P. Latinne, M. Saerens, and C. Decaestecker · 2001
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One-class SVMs for document classification
L. M. Manevitz and M. Yousef · 2001
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Classification on data with biased class distribution
S. Vucetic and Z. Obradovic · 2001
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Partially supervised classification of text documents
B. Liu, W. S. Lee, P. S. Yu, and X. Li · 2002
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Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure
M. Saerens, P. Latinne, and C. Decaestecker · 2002
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Learning with positive and unlabeled examples using weighted logistic regression
W. S. Lee and B. Liu · 2003
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Building text classifiers using positive and unlabeled examples
B. Liu, Y. Dai, X. Li, W.S. Lee, and P. S. Yu · 2003
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Statistical significance for genomewide studies
J. D. Storey and R. Tibshirani · 2003
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The curse of dimensionality and dimension reduction
D. W. Scott · 2008
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Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data
S. J. Phillips, M. Dudik, J. Elith, C. H. Graham, A. Lehmann, J. Leathwick, and S. Ferrier · 2009
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Novelty detection: unlabeled data definitely help
C. Scott and G. Blanchard · 2009
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Presence-only data and the EM algorithm
G. Ward, T. Hastie, S. Barry, J. Elith, and J.R. Leathwick · 2009
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Semi-supervised novelty detection
G. Blanchard, G. Lee, and C. Scott · 2010
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Learning from positive and unlabeled examples by enforcing statistical significance
P. Geurts · 2011
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Editorial: special issue on learning from imbalanced data sets
N. V. Chawla, N. Japkowicz, and A. Kotcz · 2004
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PEBL: web page classification without negative examples
H. Yu, J. Han, and K. C. C. Chang · 2004
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Learning from positive and unlabeled examples
F. Denis, R. Gilleron, and F. Letouzey · 2005
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A Neyman-Pearson approach to statistical learning
C. Scott and R. Nowak · 2005
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A simple probabilistic approach to learning from positive and unlabeled examples
D. Zhang and W. S. Lee · 2005
Cited alongside, same era.
Sparse nonparametric density estimation in high dimensions using the rodeo
H. Liu, J. D. Lafferty, and L. A. Wasserman · 2007
Cited alongside, same era.
Identifiability of the proportion of null hypotheses in skew-mixture models for the p-value distribution
S. Ghosal and A. Roy · 2011
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Semi-supervised learning of class balance under class-prior change by distribution matching
M. C. du Plessis and M. Sugiyama · 2012
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Finite skew-mixture models for estimation of positive false discovery rates
G. J. Beana, E. A. Dimarcoa, L. D. Mercer, L. K. Thayer, A. Roya, and S. Ghosal · 2013
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CAFA and the open world of protein function predictions
C. Dessimoz, N. Skunca, and P. D. Thomas · 2013
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UCI Machine Learning Repository, 2013
M. Lichman · 2013
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Classification with asymmetric label noise: consistency and maximal denoising
C. Scott, G. Blanchard, and G. Handy · 2013
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Class prior estimation from positive and unlabeled data
M. C. du Plessis and M. Sugiyama · 2014
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Analysis of learning from positive and unlabeled data
M. C. du Plessis, G. Niu, and M. Sugiyama · 2014
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