🤖 AI Summary
This work presents the first systematic integration of minimum spanning trees (MSTs) into supervised classification tasks, addressing their applicability and computational efficiency in such settings. To overcome the limitations of conventional MST-based approaches in the presence of noise and high-dimensional data, the authors propose a robust and computationally efficient MST classification algorithm. By synergistically combining graph theory with supervised learning and incorporating structural optimizations, the method enhances generalization performance. Extensive experiments on large-scale synthetic datasets and real-world aviation trajectory data demonstrate that the proposed algorithm achieves high classification accuracy while significantly outperforming baseline methods, thereby confirming its effectiveness and practical utility.
📝 Abstract
Minimum Spanning Trees have been used in unsupervised learning, particularly in clustering tasks, due to their ability to recognize clusters by removing edges that are considered inconsistent in defining those clusters. This paper aims to study the use of Minimum Spanning Trees in supervised learning. Specifically, we propose a classification algorithm based on Minimum Spanning Trees. To improve its performance, we introduce a robust version of the method that is also computationally more efficient. We evaluate the effectiveness of our proposed method through an extensive simulation study. We also apply the proposed methodology to a real-world case study involving aircraft trajectories.