COLT '90 Proceedings of the third annual workshop on Computational learning theory
The weighted majority algorithm
Information and Computation
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Journal of the ACM (JACM)
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A game of prediction with expert advice
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Machine Learning - Special issue on context sensitivity and concept drift
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking a small set of experts by mixing past posteriors
The Journal of Machine Learning Research
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Real-time ranking with concept drift using expert advice
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Non-stationary data sequence classification using online class priors estimation
Pattern Recognition
Boosting classifiers for drifting concepts
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters
The Journal of Machine Learning Research
LEARNING DRIFTING NEGOTIATIONS
Applied Artificial Intelligence
Categorizing and mining concept drifting data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic Detection of Learnability under Unreliable and Sparse User Feedback
Proceedings of the 30th DAGM symposium on Pattern Recognition
Leveraging Web 2.0 Sources for Web Content Classification
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A Multi-partition Multi-chunk Ensemble Technique to Classify Concept-Drifting Data Streams
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An Aggregate Ensemble for Mining Concept Drifting Data Streams with Noise
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Boosting expert ensembles for rapid concept recall
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Lacking Labels in the Stream: Classifying Evolving Stream Data with Few Labels
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Regression Trees from Data Streams with Drift Detection
DS '09 Proceedings of the 12th International Conference on Discovery Science
Learning, detecting, understanding, and predicting concept changes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Using diversity to handle concept drift in on-line learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Quick adaptation to changing concepts by sensitive detection
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Classification and novel class detection of data streams in a dynamic feature space
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Robust ensemble learning for mining noisy data streams
Decision Support Systems
Active learning from stream data using optimal weight classifier ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Gas sensor drift mitigation using classifier ensembles
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Cloud-based malware detection for evolving data streams
ACM Transactions on Management Information Systems (TMIS)
Enabling fast prediction for ensemble models on data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning with local drift detection
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Classification and novel class detection in data streams with active mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Can cross-company data improve performance in software effort estimation?
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
A social approach for learning agents
Expert Systems with Applications: An International Journal
Automated Anomaly Detector Adaptation using Adaptive Threshold Tuning
ACM Transactions on Information and System Security (TISSEC)
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Design and Implementation of a Data Mining System for Malware Detection
Journal of Integrated Design & Process Science
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We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence of the training data relative to the performance of the best expert. However, because these "experts" may be difficult to implement, we take a more general approach and bound performance relative to the actual performance of any online learner on this single subsequence. We present the additive expert ensemble algorithm AddExp, a new, general method for using any online learner for drifting concepts. We adapt techniques for analyzing expert prediction algorithms to prove mistake and loss bounds for a discrete and a continuous version of AddExp. Finally, we present pruning methods and empirical results for data sets with concept drift.