A Classification Methodology based on Subspace Graphs Learning

TL;DR

This paper introduces a novel ensemble-based classification methodology utilizing subspace graphs and spanning trees, achieving competitive results on benchmark datasets, especially with unbalanced data.

Contribution

It presents a new ensemble-of-classifiers approach using spanning trees for one-class classification, combining subspace partitioning and minimum distance concepts.

Findings

Achieves state-of-the-art or comparable results on benchmark datasets.

Performs well with unbalanced datasets.

Utilizes a novel ensemble methodology based on spanning trees.

Abstract

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.