Out-of-bag estimation of the optimal sample size in bagging

✍ Scribed by Gonzalo Martínez-Muñoz; Alberto Suárez

• Publisher: Elsevier Science
• Year: 2010
• Tongue: English
• Weight: 321 KB
• Volume: 43
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2009.05.010

✦ Synopsis

The performance of m-out-of-n bagging with and without replacement in terms of the sampling ratio (m/n) is analyzed. Standard bagging uses resampling with replacement to generate bootstrap samples of equal size as the original training set m wor = n. Without-replacement methods typically use half samples m wr = n/2. These choices of sampling sizes are arbitrary and need not be optimal in terms of the classification performance of the ensemble. We propose to use the out-of-bag estimates of the generalization accuracy to select a near-optimal value for the sampling ratio. Ensembles of classifiers trained on independent samples whose size is such that the out-of-bag error of the ensemble is as low as possible generally improve the performance of standard bagging and can be efficiently built.