基于介数中心性的网络高层数据分类算法
A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality
摘要：
数据分类是一种主要的机器学习范式，已被广泛应用于解决大量的现实问题。传统的数据分类技术只考虑物理特征（例如，距离、相似性或分布）。由于这个原因，这些被称为\textit{low-level}分类。另一方面，人类（动物）大脑执行低级和高级学习，并且它具有根据输入数据的语义含义识别模式的能力。不仅考虑物理属性而且考虑图案形成的数据分类被称为\textit{high-level}分类。已经开发了几种高级分类技术，它们利用复杂网络来表征数据模式，并取得了可喜的成果。在本文中，我们提出了一个纯粹的基于网络的高层次的分类技术，使用介数中心性措施。我们在九个不同的真实数据集上测试了该模型，并将其与其他九个传统和知名的分类模型进行了比较。结果表明，我们一个称职的分类性能。
英文摘要：
Data classification is a major machine learning paradigm, which has been widely applied to solve a large number of real-world problems. Traditional data classification techniques consider only physical features (e.g., distance, similarity, or distribution) of the input data. For this reason, those are called \textit{low-level} classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has a facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is referred to as \textit{high-level} classification. Several high-level classification techniques have been developed, which make use of complex networks to characterize data patterns and have obtained promising results. In this paper, we propose a pure network-based high-level classification technique that uses the betweenness centrality measure. We test this model in nine different real datasets and compare it with other nine traditional and well-known classification models. The results show us a competent classification performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
