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To decide if an update to a data stream classifier is necessary, existing sliding window based techniques monitor classifier performance on recent instances. If there is a significant change in classifier performance, these approaches determine a chunk boundary, and update the classifier. However, monitoring classifier performance is costly due to scarcity of labeled data. In our previous work, we...
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,...
Static data mining assumptions with regard to features and labels often fail the streaming context. Features evolve, concepts drift, and novel classes are introduced. Therefore, any classification algorithm that intends to operate on streaming data must have mechanisms to mitigate the obsolescence of classifiers trained early in the stream. This is typically accomplished by either continually updating...
Recent approaches for classifying data streams are mostly based on supervised learning algorithms, which can only be trained with labeled data. Manual labeling of data is both costly and time consuming. Therefore, in a real streaming environment where large volumes of data appear at a high speed, only a small fraction of the data can be labeled. Thus, only a limited number of instances will be available...
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