Robust boosting classification models with local sets of probability distributions

Robust classification models based on the ensemble methodology are proposed in the paper. The main feature of the models is that the precise vector of weights assigned for examples in the training set at each iteration of boosting is replaced by a local convex set of weight vectors. The minimax strategy is used for building weak classifiers at each iteration. The local sets of weights are constructed by means of imprecise statistical models. The proposed models are called RILBoost (Robust Imprecise Local Boost). Numerical experiments with real data show that the proposed models outperform the standard AdaBoost algorithm for several well-known data sets.