Depth-based classification for relational data with multiple attributes
Depth-based classification for relational data with multiple attributes
复制标题
多属性关系数据的深度分类
DOI：
10.1016/j.jmva.2021.104732
复制
发表时间：
2021
影响因子：
1.6
通讯作者：
Gel, Yulia R.
中科院分区：
文献类型：
--
作者：
Zhang, Xu;Tian, Yahui;Guan, Guoyu;Gel, Yulia R.
随着近年来数据采集技术的进步，从在线社交互动到多组学研究再到电子健康记录的链接，显示关系依赖的数据分类继续受到越来越多的关注。通过引入数据深度的鲁棒性和固有的几何概念，我们提出了一种新的几何增强分类方法，用于具有多个节点属性的复杂网络形式的关系数据。从逻辑回归开始描述类标签和节点属性之间的关系，关键方法是基于将任意两个节点之间的链接概率建模为它们的类标签及其在各自类中的数据深度的函数。然后根据类标签的后验概率得到近似预测规则。将深度概念集成到分类过程中使我们能够更好地捕获关系数据的底层几何结构，从而增强其有限样本的性能。我们推导了新分类方法的渐近性质，并通过广泛的模拟验证了它的有限样本性质。提出的几何增强分类方法在中国最大的社交媒体平台之一新浪微博的用户分析中得到了说明。
With the recent progress of data acquisition technology, classification of data exhibiting relational dependence, from online social interactions to multi-omics studies to linkage of electronic health records, continues to gain an ever increasing attention. By introducing a robust and inherently geometric concept of data depth we propose a new type of geometrically-enhanced classification method for relational data that are in a form of a complex network with multiple node attributes. Starting from a logistic regression to describe the relationship between the class labels and node attributes, the key approach is based on modeling the link probability between any two nodes as a function of their class labels and their data depths within the respective classes. The approximate prediction rule is then obtained according to the posterior probability of the class labels. Integrating the depth concept into the classification process allows us to better capture the underlying geometry of the relational data and, as a result, to enhance its finite sample performance. We derive asymptotic properties of the new classification approach and validate its finite sample properties via extensive simulations. The proposed geometrically-enhanced classification method is illustrated in application to user analysis of the one of the largest Chinese social media platforms, Sina Weibo.
登录
查看更多内容
功能数据的快速DD分类
影响因子：
1.3
作者：
K. Mosler;Pavlo Mozharovskyi
通讯作者：
Pavlo Mozharovskyi
剖析以太坊区块链分析：我们从以太坊图的拓扑和几何学到什么
DOI：
--
发表时间：
2019
期刊：
SDM
影响因子：
--
作者：
Yitao Li;Umar Islambekov;C. Akcora;Ekaterina Smirnova;Y. Gel;Murat Kantarcioglu
通讯作者：
Murat Kantarcioglu
根据类带深度进行数据分析和分类
DOI：
--
发表时间：
2003
期刊：
Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications
影响因子：
--
作者：
K. Mosler;R. Hoberg
通讯作者：
R. Hoberg
影响因子：
5.7
作者：
Nieto-Reyes, Alicia;Battey, Heather
通讯作者：
Battey, Heather
影响因子：
4.5
作者：
Regina Y. Liu;J. Parelius;Kesar Singh
通讯作者：
Regina Y. Liu;J. Parelius;Kesar Singh