Online bagging and boosting
Oza, N.C.
Systems, Man and Cybernetics, 2005 IEEE International Conference on
Volume 3, Issue , 10-12 Oct. 2005 Page(s): 2340 - 2345 Vol. 3
Digital Object Identifier   10.1109/ICSMC.2005.1571498
Summary: Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. However, these algorithms have been used mainly in batch mode, i.e., they require the entire training set to be available at once and, in some cases, require random access to the data. In this paper, we present online versions of bagging and boosting that require only one pass through the training data. We build on previously presented work by describing some theoretical results. We also compare the online and batch algorithms experimentally in terms of accuracy and running time.

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