Abstract:Unlike traditional data mining where data is static, mining algorithms for data streams must process the data "on the fly" and update the class decision boundaries as the stream progresses to address the challenges of concept drift and feature evolution. In our current work, we have proposed a multi-tiered ensemble based fast and robust method, which rapidly learns the concepts in a data stream, predicts labels for new data with strong accuracy, and agilely tracks the dynamic changes in the evolving concepts a… Show more
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