A generalised label noise model for classification in the presence of annotation errors

[1]  P. Lachenbruch Discriminant Analysis When the Initial Samples Are Misclassified , 1966 .

[2]  P. Lachenbruch Discriminant Analysis When the Initial Samples are Misclassified II: Non-Random Misclassification Models , 1974 .

[3]  R. Chhikara,et al.  Linear discriminant analysis with misallocation in training samples , 1984 .

[4]  Gábor Lugosi,et al.  Learning with an unreliable teacher , 1992, Pattern Recognit..

[5]  Edith Cohen,et al.  Learning noisy perceptrons by a perceptron in polynomial time , 1997, Proceedings 38th Annual Symposium on Foundations of Computer Science.

[6]  Thierry Denoeux,et al.  Analysis of evidence-theoretic decision rules for pattern classification , 1997, Pattern Recognit..

[7]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[8]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Sanjoy Dasgupta,et al.  Learning mixtures of Gaussians , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).

[10]  Thierry Denoeux,et al.  A neural network classifier based on Dempster-Shafer theory , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[11]  R. Spang,et al.  Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Bernhard Schölkopf,et al.  Estimating a Kernel Fisher Discriminant in the Presence of Label Noise , 2001, ICML.

[13]  Enrico Blanzieri,et al.  Detecting potential labeling errors in microarrays by data perturbation , 2006, Bioinform..

[14]  G. Tian,et al.  Statistical Applications in Genetics and Molecular Biology Sparse Logistic Regression with Lp Penalty for Biomarker Identification , 2011 .

[15]  Brendan T. O'Connor,et al.  Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.

[16]  Thierry Denoeux A k -Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory , 2008, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[17]  Beata Beigman Klebanov,et al.  Squibs: From Annotator Agreement to Noise Models , 2009, CL.

[18]  Beata Beigman Klebanov,et al.  Learning with Annotation Noise , 2009, ACL.

[19]  Gordon V. Cormack,et al.  Genre-based decomposition of email class noise , 2009, KDD.

[20]  Thierry Denoeux,et al.  Learning from partially supervised data using mixture models and belief functions , 2009, Pattern Recognit..

[21]  E. Lesaffre,et al.  Correcting for misclassification for a monotone disease process with an application in dental research , 2010, Statistics in medicine.

[22]  Gerardo Hermosillo,et al.  Learning From Crowds , 2010, J. Mach. Learn. Res..

[23]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[24]  Benoît Frénay,et al.  Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs , 2011, ECML/PKDD.

[25]  Ata Kabán,et al.  Multi-class classification in the presence of labelling errors , 2011, ESANN.

[26]  Ata Kabán,et al.  Label-Noise Robust Logistic Regression and Its Applications , 2012, ECML/PKDD.

[27]  Manuel Martín-Merino,et al.  A kernel SVM algorithm to detect mislabeled microarrays in human cancer samples , 2013, 13th IEEE International Conference on BioInformatics and BioEngineering.

[28]  Nagarajan Natarajan,et al.  Learning with Noisy Labels , 2013, NIPS.

[29]  Ata Kabán,et al.  Classification of mislabelled microarrays using robust sparse logistic regression , 2013, Bioinform..

[30]  Genevera I. Allen,et al.  Molecular pathway identification using biological network-regularized logistic models , 2013, BMC Genomics.

[31]  Christopher D. Manning,et al.  Robust Logistic Regression using Shift Parameters , 2013, ACL.

[32]  Ata Kabán,et al.  Learning kernel logistic regression in the presence of class label noise , 2014, Pattern Recognition.

[33]  M. Verleysen,et al.  Classification in the Presence of Label Noise: A Survey , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[34]  Aritra Ghosh,et al.  Making risk minimization tolerant to label noise , 2014, Neurocomputing.