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 predictive model. Revising the predict...

AdaBoost [4] is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step in AdaBoost is constructing a distribution over the training examples to create each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by the previous base model in the sequence [6]. The idea is to ma...