  Noise is a fundamental problem in learning theory with huge effects in the
application of Machine Learning (ML) methods, due to real world data tendency
to be noisy. Additionally, introduction of malicious noise can make ML methods
fail critically, as is the case with adversarial attacks. Thus, finding and
developing alternatives to improve robustness to noise is a fundamental problem
in ML. In this paper, we propose a method to deal with noise: mitigating its
effect through the use of data abstractions. The goal is to reduce the effect
of noise over the model's performance through the loss of information produced
by the abstraction. However, this information loss comes with a cost: it can
result in an accuracy reduction due to the missing information. First, we
explored multiple methodologies to create abstractions, using the training
dataset, for the specific case of numerical data and binary classification
tasks. We also tested how these abstractions can affect robustness to noise
with several experiments that explore the robustness of an Artificial Neural
Network to noise when trained using raw data \emph{vs} when trained using
abstracted data. The results clearly show that using abstractions is a viable
approach for developing noise robust ML methods.
