Dual perturb and combine algorithm

In this paper, a dual perturb and combine algorithm is proposed which consists in producing the perturbed predictions at the prediction stage using only one model. To this end, the attribute vector of a test case is perturbed several times by an additive random noise, the model is applied to each of these perturbed vectors and the resulting predictions are aggregated. An analytical version of this algorithm is described in the context of decision tree induction. From experiments on several datasets, it appears that this simple algorithm yields signiicant improvements on several problems, sometimes comparable to those obtained with bagging. When combined with decision tree bagging, this algorithm also improves accuracy in many problems .