Learning in the presence of concept drift and hidden contexts
Machine Learning
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy 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
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Learning from Noisy Data
Machine Learning
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Tackling concept drift by temporal inductive transfer
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Real-time ranking with concept drift using expert advice
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning on the border: active learning in imbalanced data classification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Adaptive spike detection for resilient data stream mining
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Learning Decision Trees for Unbalanced Data
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Detecting Fractures in Classifier Performance
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
ACE: adaptive classifiers-ensemble system for concept-drifting environments
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Learning in non-stationary environments with class imbalance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Streaming data is pervasive in a multitude of data mining applications. One fundamental problem in the task of mining streaming data is distributional drift over time. Streams may also exhibit high and varying degrees of class imbalance, which can further complicate the task. In scenarios like these, class imbalance is particularly difficult to overcome and has not been as thoroughly studied. In this paper, we comprehensively consider the issues of changing distributions in conjunction with high degrees of class imbalance in streaming data. We propose new approaches based on distributional divergence and meta-classification that improve several performance metrics often applied in the study of imbalanced classification. We also propose a new distance measure for detecting distributional drift and examine its utility in weighting ensemble base classifiers. We employ a sequential validation framework, which we believe is the most meaningful option in the context of streaming imbalanced data.