用最小生成树做分类，提升效率与鲁棒性
A new classification method based on Minimum Spanning Trees
- 基于最小生成树构建分类算法，通过剔除不一致边识别类别
- 新方法在模拟和真实飞机轨迹数据上表现更优
- 适合需要高效鲁棒分类的工程应用
最小生成树因其能通过移除定义聚类不一致的边来识别簇，常用于无监督学习中的聚类任务。本文研究其在有监督学习中的应用，提出一种基于最小生成树的分类算法。为提升性能，引入一种更稳健且计算更高效的改进版本。通过大规模模拟研究评估了该方法的有效性，并将其应用于真实的飞机轨迹案例研究。
原文摘要 · Abstract (English)
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.
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