Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine Learning - Special issue on context sensitivity and concept drift
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Selecting Examples for Partial Memory Learning
Machine Learning
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
Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning decision trees from dynamic data streams
Proceedings of the 2005 ACM symposium on Applied computing
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
Decision trees for mining data streams
Intelligent Data Analysis
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Real-time ranking with concept drift using expert advice
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Online Mass Flow Prediction in CFB Boilers
ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
Handling outliers and concept drift in online mass flow prediction in CFB boilers
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers
DS '09 Proceedings of the 12th International Conference on Discovery Science
Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift
ACM SIGKDD Explorations Newsletter
Mining concept-drifting data streams containing labeled and unlabeled instances
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Learning model trees from evolving data streams
Data Mining and Knowledge Discovery
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?
Expert Systems with Applications: An International Journal
Artificial recurrence for classification of streaming data with concept shift
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Classifying evolving data streams with partially labeled data
Intelligent Data Analysis
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Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the distribution that generates the examples changes over time. We present a method for detection of changes in the probability distribution of examples. The idea behind the drift detection method is to monitor the online error-rate of a learning algorithm looking for significant deviations. The method can be used as a wrapper over any learning algorithm. In most problems, a change affects only some regions of the instance space, not the instance space as a whole. In decision models that fit different functions to regions of the instance space, like Decision Trees and Rule Learners, the method can be used to monitor the error in regions of the instance space, with advantages of fast model adaptation. In this work we present experiments using the method as a wrapper over a decision tree and a linear model, and in each internal-node of a decision tree. The experimental results obtained in controlled experiments using artificial data and a real-world problem show a good performance detecting drift and in adapting the decision model to the new concept.