Learning in the presence of concept drift and hidden contexts
Machine Learning
The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Classification for Imprecise Environments
Machine Learning
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Study on the Effect of Class Distribution Using Cost-Sensitive Learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Class Noise vs. Attribute Noise: A Quantitative Study
Artificial Intelligence Review
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Asymptotic Bayesian generalization error when training and test distributions are different
Proceedings of the 24th international conference on Machine learning
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Impact of imputation of missing values on classification error for discrete data
Pattern Recognition
Conceptual equivalence for contrast mining in classification learning
Data & Knowledge Engineering
Dataset Shift in Machine Learning
Dataset Shift in Machine Learning
A framework for monitoring classifiers’ performance: when and why failure occurs?
Knowledge and Information Systems
Multiple ellipses detection in noisy environments: A hierarchical approach
Pattern Recognition
Improving Classification under Changes in Class and Within-Class Distributions
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Learning from labeled and unlabeled data: an empirical study across techniques and domains
Journal of Artificial Intelligence Research
Selection-fusion approach for classification of datasets with missing values
Pattern Recognition
Model Monitor (M2): Evaluating, Comparing, and Monitoring Models
The Journal of Machine Learning Research
Discriminative Learning Under Covariate Shift
The Journal of Machine Learning Research
Assessing the impact of changing environments on classifier performance
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Machine learning in adversarial environments
Machine Learning
The security of machine learning
Machine Learning
Mining With Noise Knowledge: Error-Aware Data Mining
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Information Sciences: an International Journal
Class distribution estimation based on the Hellinger distance
Information Sciences: an International Journal
Uncertainty-based learning of acoustic models from noisy data
Computer Speech and Language
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Information Sciences: an International Journal
Information Sciences: an International Journal
Aggregative quantification for regression
Data Mining and Knowledge Discovery
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The field of dataset shift has received a growing amount of interest in the last few years. The fact that most real-world applications have to cope with some form of shift makes its study highly relevant. The literature on the topic is mostly scattered, and different authors use different names to refer to the same concepts, or use the same name for different concepts. With this work, we attempt to present a unifying framework through the review and comparison of some of the most important works in the literature.