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Data mining research has produced a significant repertoire of algorithms to predict the classification of data instances with reasonable accuracy. However, data quantity and availability is continuing to rapidly expand such that we no longer have fixed and manageable data sets, but rather continual streams of data. Mining streaming data becomes challenging when using a piece-wise or online approach,...
In our current work, we have proposed a multi-tiered ensemble based robust method to address all of the challenges of labeling instances in evolving data stream. Bottleneck of our current work is, it needs to build ADABOOST ensembles for each of the numeric features. This can face scalability issue as number of features can be very large at times in data stream. In this paper, we propose an intelligent...
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