Barber, Michael J., authorHowe, Adele E., advisorAnderson, Charles, committee memberHoeting, Jennifer, committee member2007-01-032007-01-032012http://hdl.handle.net/10217/67994The task of instance classification within very large data streams is challenged by both the overwhelming amount of data, and a phenomenon known as concept drift. In this research we provide a comprehensive comparison of several state of the art ensemble methods that purport to handle concept drift, and we propose two additional algorithms. Our two new methods, the AMPE and AMPE2 algorithms are then used to further our understanding of concept drift and the algorithmic factors that influence the performance of ensemble based concept drift algorithms.born digitalmasters thesesengdata miningonline analysismachine learningensemblesTextThis material is open access and distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0).