Suppressing model overfitting in mining concept-drifting data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Practical learning from one-sided feedback
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Structure and Majority Classes in Decision Tree Learning
The Journal of Machine Learning Research
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
On Appropriate Assumptions to Mine Data Streams: Analysis and Practice
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Time stamping in the presence of latency and drift
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
The algorithm APT to classify in concurrence of latency and drift
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Drift mining in data: A framework for addressing drift in classification
Computational Statistics & Data Analysis
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Online classification learners operating under concept drift can be subject to latency in examples arriving at the training base. A discussion of latency and the related notion of example filtering leads to the development of an example life cycle for online learning (OLLC). Latency in a data stream is modelled in a new Example Life-cycle Integrated Simulation Environment (ELISE). In a series of experiments, the online learner algorithm CD3 is evaluated under several drift and latency scenarios. Results show that systems subject to large random latencies can, when drift occurs, suffer substantial deterioration in classification rate with slow recovery.