Using adaptive bagging to debias regressions
Breiman showed that bagging could effectively reduce the variance of regression predictors, while leaving the bias unchanged. A new form of bagging we call adaptive bagging is effective in reducing both bias and variance. The procedure works in stages--the first stage is bagging. Based on the outcomes of the first stage, the output values are altered and a second stage of bagging is carried out using the altered output values. This is repeated until a specified noise level is reached. We give the background theory, and test the method using both trees and nearest neighbor regression methods. Application to two class classification data gives some interesting results.