C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Suppressing model overfitting in mining concept-drifting data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Evolving fuzzy classifiers using different model architectures
Fuzzy Sets and Systems
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
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
The impact of latency on online classification learning with concept drift
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
Applied Soft Computing
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Online classification learners operating under concept drift can be subject to latency in example arrival at the training base. The impact of such latency on the definition of a time stamp is discussed against the background of the Online Learning Life Cycle (OLLC). Data stream latency is modeled in Example Life-cycle Integrated Simulation Environment (ELISE). Two new algorithms are presented: CDTC versions 1 and 2, in which a specific time stamp protocol is used representing the time of classification. Comparison of these algorithms against previous time stamp learning algorithms CD3 and CD5 is made. A time stamp definition and algorithmic solution is presented for handling latency in data streams and improving classification recovery in such affected domains.