  In a standard classification framework a set of trustworthy learning data are
employed to build a decision rule, with the final aim of classifying unlabelled
units belonging to the test set. Therefore, unreliable labelled observations,
namely outliers and data with incorrect labels, can strongly undermine the
classifier performance, especially if the training size is small. The present
work introduces a robust modification to the Model-Based Classification
framework, employing impartial trimming and constraints on the ratio between
the maximum and the minimum eigenvalue of the group scatter matrices. The
proposed method effectively handles noise presence in both response and
exploratory variables, providing reliable classification even when dealing with
contaminated datasets. A robust information criterion is proposed for model
selection. Experiments on real and simulated data, artificially adulterated,
are provided to underline the benefits of the proposed method.
