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Effective classification of noisy data streams with attribute-oriented dynamic classifier selection
2005
Knowledge and Information Systems
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
doi:10.1007/s10115-005-0212-y
fatcat:ndlcau4kqzgx5bjuxqud2fjle4