高维数据特征筛选算法

Q3 Mathematics
Hasna Chamlal, A. Benzmane, T. Ouaderhman
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引用次数: 0

摘要

目前,特征筛选正在成为机器学习和高维数据分析领域的一个重要课题。从一组变量中过滤出不相关的特征被认为是在进行任何数据分析之前应该执行的重要的初步步骤。在范和吕(J. Royal Stat. Soc)的工作之后,对同一主题提出了许多方法。,爵士。B. 70(5), 849-911(2008)),他介绍了确定的筛选特性。然而,这些方法的性能因论文而异。在这项工作中,我们的目标是在这个列表中添加一个受Kendall交互过滤器启发的执行特征筛选的新算法。当响应变量为连续时,Stat. 50(7), 1496-1514(2020))。通过与本工作中提出的现有方法在几种仿真场景下的比较,证明了该算法的良好性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature screening algorithm for high dimensional data
Currently, feature screening is becoming an important topic in the fields of machine learning and high-dimensional data analysis. Filtering out irrelevant features from a set of variables is considered to be an important preliminary step that should be performed before any data analysis. Many approaches have been proposed to the same topic after the work of Fan and Lv (J. Royal Stat. Soc., Ser. B. 70 (5), 849–911 (2008)), who introduced the sure screening property. However, the performance of these methods differs from one paper to another. In this work, we aim to add to this list a new algorithm performing feature screening inspired by the Kendall interaction filter (J. Appl. Stat. 50 (7), 1496–1514 (2020)) when the response variable is continuous. The good behavior of our algorithm is proved through a comparison with an existing method, proposed in this work under several simulation scenarios.
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来源期刊
Mathematical Modeling and Computing
Mathematical Modeling and Computing Computer Science-Computational Theory and Mathematics
CiteScore
1.60
自引率
0.00%
发文量
54
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