A Classification Methodology based on Subspace Graphs Learning

--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. 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. In Machine Learning, the usage of multiple classifiers (ensemble-of-classifiers) is a well-known technique employed to get a boost on performance compared to single classifiers [1]. Even though it has been applied to different scenarios with great results, the sensitivity of the system on long tailed datasets still remains an open problem [2].