A new classification method based on Minimum Spanning Trees
For researchers in supervised learning, this work extends the use of Minimum Spanning Trees from clustering to classification, but the improvements appear incremental.
The paper proposes a classification algorithm based on Minimum Spanning Trees and introduces a robust, computationally efficient version. Simulation studies and a real-world case study on aircraft trajectories demonstrate its effectiveness.
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.