  In this paper, we propose a design methodology for one-class classifiers
using an ensemble-of-classifiers approach. The objective is to select the best
structures created during the training phase using an ensemble of spanning
trees. It takes the best classifier, partitioning the area near a pattern into
$\gamma^{\gamma-2}$ sub-spaces and combining all possible spanning trees that
can be created starting from $\gamma$ nodes. The proposed method leverages on a
supervised classification methodology and the concept of minimum distance. We
evaluate our approach on well-known benchmark datasets and results obtained
demonstrate that it achieves comparable and, in many cases, state-of-the-art
results. Moreover, it obtains good performance even with unbalanced datasets.
