在密集数据分析中检测关键变量的组合方法
A Combined Approach To Detect Key Variables In Thick Data Analytics
摘要：
在机器学习中，策略性任务之一是仅选择重要变量作为响应的预测因子。在本文中，提出了一种方法，它包括在候选预测变量的置换测试的应用程序中，只确定最翔实的目的。这种方法可以解决一些工业问题，并介绍了在化学分析领域的应用。提出的方法和拉索，这是在文献中提供的功能选择的最常见的替代品之一之间进行比较。
英文摘要：
In machine learning one of the strategic tasks is the selection of only significant variables as predictors for the response(s). In this paper an approach is proposed which consists in the application of permutation tests on the candidate predictor variables in the aim of identifying only the most informative ones. Several industrial problems may benefit from such an approach, and an application in the field of chemical analysis is presented. A comparison is carried out between the approach proposed and Lasso, that is one of the most common alternatives for feature selection available in the literature.
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。
