  In this paper we propose a novel methodology to construct Optimal
Classification Trees that takes into account that noisy labels may occur in the
training sample. Our approach rests on two main elements: (1) the splitting
rules for the classification trees are designed to maximize the separation
margin between classes applying the paradigm of SVM; and (2) some of the labels
of the training sample are allowed to be changed during the construction of the
tree trying to detect the label noise. Both features are considered and
integrated together to design the resulting Optimal Classification Tree. We
present a Mixed Integer Non Linear Programming formulation for the problem,
suitable to be solved using any of the available off-the-shelf solvers. The
model is analyzed and tested on a battery of standard datasets taken from UCI
Machine Learning repository, showing the effectiveness of our approach.
