  ProBoost, a new boosting algorithm for probabilistic classifiers, is proposed
in this work. This algorithm uses the epistemic uncertainty of each training
sample to determine the most challenging/uncertain ones; the relevance of these
samples is then increased for the next weak learner, producing a sequence that
progressively focuses on the samples found to have the highest uncertainty. In
the end, the weak learners' outputs are combined into a weighted ensemble of
classifiers. Three methods are proposed to manipulate the training set:
undersampling, oversampling, and weighting the training samples according to
the uncertainty estimated by the weak learners. Furthermore, two approaches are
studied regarding the ensemble combination. The weak learner herein considered
is a standard convolutional neural network, and the probabilistic models
underlying the uncertainty estimation use either variational inference or Monte
Carlo dropout. The experimental evaluation carried out on MNIST benchmark
datasets shows that ProBoost yields a significant performance improvement. The
results are further highlighted by assessing the relative achievable
improvement, a metric proposed in this work, which shows that a model with only
four weak learners leads to an improvement exceeding 12% in this metric (for
either accuracy, sensitivity, or specificity), in comparison to the model
learned without ProBoost.
