A unifying view on dataset shift in classification

[1]  Jose Garcia Moreno-Torres,et al.  Repairing fractures between data using genetic programming-based feature extraction: A case study in cancer diagnosis , 2013, Inf. Sci..

[2]  Amos Storkey,et al.  When Training and Test Sets are Different: Characterising Learning Transfer , 2013 .

[3]  Blaine Nelson,et al.  The security of machine learning , 2010, Machine Learning.

[4]  Richard Lippmann,et al.  Machine learning in adversarial environments , 2010, Machine Learning.

[5]  Fabio Roli,et al.  Multiple classifier systems for robust classifier design in adversarial environments , 2010, Int. J. Mach. Learn. Cybern..

[6]  Ming Dong,et al.  Selection-fusion approach for classification of datasets with missing values , 2010, Pattern Recognit..

[7]  Francisco Herrera,et al.  A study on the use of imputation methods for experimentation with Radial Basis Function Network classifiers handling missing attribute values: The good synergy between RBFNs and EventCovering method , 2010, Neural Networks.

[8]  Nitesh V. Chawla,et al.  Model Monitor (M2): Evaluating, Comparing, and Monitoring Models , 2009, J. Mach. Learn. Res..

[9]  Steffen Bickel,et al.  Discriminative Learning Under Covariate Shift , 2009, J. Mach. Learn. Res..

[10]  Hong Qiao,et al.  Multiple ellipses detection in noisy environments: A hierarchical approach , 2009, Pattern Recognit..

[11]  Jesús Cid-Sueiro,et al.  Improving Classification under Changes in Class and Within-Class Distributions , 2009, IWANN.

[12]  Andrew K. C. Wong,et al.  Classification of Imbalanced Data: a Review , 2009, Int. J. Pattern Recognit. Artif. Intell..

[13]  Neil D. Lawrence,et al.  Dataset Shift in Machine Learning , 2009 .

[14]  Karsten M. Borgwardt,et al.  Covariate Shift by Kernel Mean Matching , 2009, NIPS 2009.

[15]  S. Roweis,et al.  An Adversarial View of Covariate Shift and A Minimax Approach , 2009 .

[16]  Neil D. Lawrence,et al.  When Training and Test Sets Are Different: Characterizing Learning Transfer , 2009 .

[17]  David A. Cieslak,et al.  A framework for monitoring classifiers’ performance: when and why failure occurs? , 2009, Knowledge and Information Systems.

[18]  Lukasz A. Kurgan,et al.  Impact of imputation of missing values on classification error for discrete data , 2008, Pattern Recognit..

[19]  Xindong Wu,et al.  Conceptual equivalence for contrast mining in classification learning , 2008, Data Knowl. Eng..

[20]  Nathalie Japkowicz,et al.  Assessing the Impact of Changing Environments on Classifier Performance , 2008, Canadian Conference on AI.

[21]  Xindong Wu,et al.  Mining With Noise Knowledge: Error-Aware Data Mining , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[22]  Yang Wang,et al.  Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..

[23]  Klaus-Robert Müller,et al.  Covariate Shift Adaptation by Importance Weighted Cross Validation , 2007, J. Mach. Learn. Res..

[24]  Steffen Bickel,et al.  Discriminative learning for differing training and test distributions , 2007, ICML '07.

[25]  Motoaki Kawanabe,et al.  Asymptotic Bayesian generalization error when training and test distributions are different , 2007, ICML '07.

[26]  Jesús Cid-Sueiro,et al.  Minimax Regret Classifier for Imprecise Class Distributions , 2007, J. Mach. Learn. Res..

[27]  Bernhard Schölkopf,et al.  Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.

[28]  D. Hand Rejoinder: Classifier Technology and the Illusion of Progress , 2006, math/0606461.

[29]  T. Ho,et al.  Data Complexity in Pattern Recognition , 2006 .

[30]  Nitesh V. Chawla,et al.  Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains , 2011, J. Artif. Intell. Res..

[31]  Rich Caruana,et al.  Predicting good probabilities with supervised learning , 2005, ICML.

[32]  Gerhard Widmer,et al.  Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.

[33]  Geoffrey I. Webb,et al.  On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions , 2005, Machine Learning.

[34]  Peter A. Flach,et al.  A Response to Webb and Ting’s On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions , 2005, Machine Learning.

[35]  Pedro M. Domingos,et al.  Adversarial classification , 2004, KDD.

[36]  KlinkenbergRalf Learning drifting concepts: Example selection vs. example weighting , 2004 .

[37]  Ralf Klinkenberg,et al.  Learning drifting concepts: Example selection vs. example weighting , 2004, Intell. Data Anal..

[38]  Bianca Zadrozny,et al.  Learning and evaluating classifiers under sample selection bias , 2004, ICML.

[39]  Jonathan Crook,et al.  Does reject inference really improve the performance of application scoring models , 2004 .

[40]  Xingquan Zhu,et al.  Class Noise vs. Attribute Noise: A Quantitative Study , 2003, Artificial Intelligence Review.

[41]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[42]  Stan Matwin,et al.  Machine Learning for the Detection of Oil Spills in Satellite Radar Images , 1998, Machine Learning.

[43]  Nicole A. Lazar,et al.  Statistical Analysis With Missing Data , 2003, Technometrics.

[44]  Jonathan Crook,et al.  Sample selection bias in credit scoring models , 2003, J. Oper. Res. Soc..

[45]  Jeffrey Xu Yu,et al.  Mining Changes of Classification by Correspondence Tracing , 2003, SDM.

[46]  Kai Ming Ting,et al.  A Study on the Effect of Class Distribution Using Cost-Sensitive Learning , 2002, Discovery Science.

[47]  Tin Kam Ho,et al.  Complexity Measures of Supervised Classification Problems , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[48]  Marco Saerens,et al.  Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure , 2002, Neural Computation.

[49]  Bianca Zadrozny,et al.  Learning and making decisions when costs and probabilities are both unknown , 2001, KDD '01.

[50]  Bianca Zadrozny,et al.  Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers , 2001, ICML.

[51]  H. Shimodaira,et al.  Improving predictive inference under covariate shift by weighting the log-likelihood function , 2000 .

[52]  Robert C. Holte,et al.  Explicitly representing expected cost: an alternative to ROC representation , 2000, KDD '00.

[53]  Niall M. Adams,et al.  The impact of changing populations on classifier performance , 1999, KDD '99.

[54]  Carla E. Brodley,et al.  Identifying Mislabeled Training Data , 1999, J. Artif. Intell. Res..

[55]  Niall M. Adams,et al.  Comparing classifiers when the misallocation costs are uncertain , 1999, Pattern Recognit..

[56]  Carla E. Brodley,et al.  Approaches to Online Learning and Concept Drift for User Identification in Computer Security , 1998, KDD.

[57]  Patrick A. Puhani,et al.  The Heckman Correction for Sample Selection and Its Critique - A Short Survey , 2000 .

[58]  David J. Hand,et al.  Statistical Classification Methods in Consumer Credit Scoring: a Review , 1997 .

[59]  D. Relles,et al.  Tools for intuition about sample selection bias and its correction , 1997 .

[60]  J. Heckman Sample selection bias as a specification error , 1979 .

[61]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.