DynaMMo: mining and summarization of coevolving sequences with missing values
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Mining data streams with concept drifts using genetic algorithm
Artificial Intelligence Review
An efficient ensemble method for classifying skewed data streams
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Classifier Ensemble for Imbalanced Data Stream Classification
Proceedings of the CUBE International Information Technology Conference
Data stream classification with artificial endocrine system
Applied Intelligence
Classifying evolving data streams with partially labeled data
Intelligent Data Analysis
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Classification is an important data analysis tool that uses a model built from historical data to predict class labels for new observations. More and more applications are featuring data streams, rather than finite stored data sets, which are a challenge for traditional classification algorithms. Concept drifts and skewed distributions, two common properties of data stream applications, make the task of learning in streams difficult. The authors aim to develop a new approach to classify skewed data streams that uses an ensemble of models to match the distribution over under-samples of negatives and repeated samples of positives.