Parallelizing Boosting and Bagging
نویسندگان
چکیده
Bagging and boosting are two general techniques for building predictors based on small samples from a dataset. We show that boosting can be parallelized, and then present performance results for parallelized bagging and boosting using OC1 decision trees and two standard datasets. The main results are that sample sizes limit achievable accuracy, regardless of computational time spent; that parallel boosting is more accurate than parallel bagging; and (unexpectedly) that parallel boosting is also cheaper than parallel bagging (at least over OC1).
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