Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Classifying Evolving Data Streams Using Dynamic Streaming Random Forests
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Data Mining and Knowledge Discovery
OcVFDT: one-class very fast decision tree for one-class classification of data streams
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Mining multi-label concept-drifting data streams using ensemble classifiers
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Editorial: Classifying text streams by keywords using classifier ensemble
Data & Knowledge Engineering
Classifier ensemble for uncertain data stream classification
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Combining analytic kernel models for energy-efficient data modeling and classification
The Journal of Supercomputing
Dynamic multi-objective evolution of classifier ensembles for video face recognition
Applied Soft Computing
Learning from data streams with only positive and unlabeled data
Journal of Intelligent Information Systems
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Recently, mining from data streams has become an important and challenging task for many real-world applications such as credit card fraud protection and sensor networking. One popular solution is to separate stream data into chunks, learn a base classifier from each chunk, and then integrate all base classifiers for effective classification. In this paper, we propose a new dynamic classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm dynamically selects a single "best" classifier to classify each test instance at run time. Our scheme uses statistical information from attribute values, and uses each attribute to partition the evaluation set into disjoint subsets, followed by a procedure that evaluates the classification accuracy of each base classifier on these subsets. Given a test instance, its attribute values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the highest classification accuracy on those subsets is selected to classify the test instance. Experimental results and comparative studies demonstrate the efficiency and efficacy of our method. Such a DCS scheme appears to be promising in mining data streams with dramatic concept drifting or with a significant amount of noise, where the base classifiers are likely conflictive or have low confidence.