Patricia E. N. Lutu

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Data stream mining is the process of applying data mining methods to a data stream in real-time in order to create descriptive or predictive models. Due to the dynamic nature of data streams, new classes may emerge as a data stream evolves, and the concept being modelled may change with time. This gives rise to the need to continuously make revisions to the(More)
—Data stream mining is the process of applying data mining methods to a data stream in real-time in order to create descriptive or predictive models. Due to the dynamic nature of data streams, new classes may emerge as a data stream evolves, and the concept being modeled may change with time. This gives rise to the need to continuously make revisions to the(More)
Classification modeling is commonly used for predictive data mining to create models (classifiers) that can predict the values of qualitative variables. Ensemble classification is concerned with the creation of many base classifiers which are then combined into one predictive classification model. Positive-versus-negative (pVn) classification has recently(More)