Randomizing Outputs to Increase Prediction Accuracy

Bagging and boosting reduce error by changing both the inputs and outputs to form perturbed training sets, grow predictors on these perturbed training sets and combine them. A question that has been frequently asked is whether it is possible to get comparable performance by perturbing the outputs alone. Two methods of randomizing outputs are experimented with. One is called output smearing and the other output flipping. Both are shown to consistently do better than bagging.

• Type: Journal
• Year: 2000
• Where: ML
• Authors: Leo Breiman
• Keywords: Comparable Performance, Machine Learning, ML 2000, Perturbed Training, Training Sets