Bagging Predictors

by: Leo Breiman

Machine Learning, Vol. 24, No. 2. (1996), pp. 123-140.

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Abstract

Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear...

@article{citeulike:167627, abstract = {Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear...}, author = {Breiman, Leo }, citeulike-article-id = {167627}, journal = {Machine Learning}, keywords = {decision\_tree, machine\_learning}, number = {2}, pages = {123--140}, posted-at = {2008-03-12 22:48:53}, priority = {2}, title = {Bagging Predictors}, url = {http://citeseer.ist.psu.edu/breiman96bagging.html}, volume = {24}, year = {1996} }

RIS record

TY - JOUR ID - citeulike:167627 L3 - citeulike-article-id:167627 TI - Bagging Predictors JF - Machine Learning VL - 24 IS - 2 SP - 123 EP - 140 N2 - Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear... KW - decision_tree KW - machine_learning AU - Breiman, Leo PY - 1996/// UR - http://citeseer.ist.psu.edu/breiman96bagging.html ER -

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