document: A streaming ensemble algorithm ( SEA ) for large-scale classification Ensemble methods have recently garnered a great deal of attention in the machine learning community . Techniques such as Boosting and Bagging have proven to be highly effective but require repeated resampling of the training data , making them inappropriate in a data mining context . The methods presented in this paper take advantage of plentiful data , building separate classifiers on sequential chunks of training points . These classifiers are combined into a fixed-size ensemble using a heuristic replacement strategy . The result is a fast algorithm for large-scale or streaming data that classifies as well as a single decision tree built on all the data , requires approximately constant memory , and adjusts quickly to concept drift .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O