  A typical issue in Pattern Recognition is the non-uniformly sampled data,
which modifies the general performance and capability of machine learning
algorithms to make accurate predictions. Generally, the data is considered
non-uniformly sampled when in a specific area of data space, they are not
enough, leading us to misclassification problems. This issue cut down the goal
of the one-class classifiers decreasing their performance. In this paper, we
propose a one-class classifier based on the minimum spanning tree with a
dynamic decision boundary (OCdmst) to make good prediction also in the case we
have non-uniformly sampled data. To prove the effectiveness and robustness of
our approach we compare with the most recent one-class classifier reaching the
state-of-the-art in most of them.
