论文标题
一种基于网络的高级数据分类算法，使用中心性
A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality
论文作者
论文摘要
数据分类是一种主要的机器学习范式，已广泛应用于解决许多现实世界中的问题。传统的数据分类技术仅考虑输入数据的物理特征（例如距离，相似性或分布）。因此，这些被称为\ textit {低级别}分类。另一方面，人（动物）大脑同时进行低水平和高阶学习，并且它具有根据输入数据的语义含义来识别模式的设施。数据分类不仅考虑物理属性，而且考虑模式形成的数据分类称为\ textit {高级}分类。已经开发了几种高级分类技术，这些技术利用复杂的网络来表征数据模式并获得了有希望的结果。在本文中，我们提出了一种纯粹的基于网络的高级分类技术，该技术使用中心度度量。我们在九个不同的实际数据集中测试了该模型，并将其与其他九种传统和众所周知的分类模型进行比较。结果向我们展示了合格的分类性能。
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.
