A Classification Methodology based on Subspace Graphs Learning
This addresses classification challenges for domains with imbalanced or limited data, though it appears incremental as it builds on existing ensemble and graph-based approaches.
The paper tackles one-class classification by proposing an ensemble method that selects optimal structures from spanning trees created during training, partitioning pattern neighborhoods into subspaces. The method achieves state-of-the-art results on benchmark datasets and performs well with unbalanced data.
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 $γ^{γ-2}$ sub-spaces and combining all possible spanning trees that can be created starting from $γ$ 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.