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
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Adaptive concept drift detection
Statistical Analysis and Data Mining - Best of SDM'09
Introduction to Machine Learning
Introduction to Machine Learning
Artificial recurrence for classification of streaming data with concept shift
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
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The traditional classifier cannot keep its quality, when the concept drift appears. The paper proposes how to protect against classification quality decreasing when concept drift occurs. Invented methods do not train classifiers all the time but they try to use earlier gained knowledge about models and switched older model to suitable new one. In this work we assume that the set of models is known and stored as the pool of classifiers. Then, by using drift detecting and searching models methods, we can choose the best model. Our propositions and the main characteristics of them were evaluated on the basis of the experiments which were carried out on chosen artificial data set.