论文标题
检测厚数据分析中关键变量的组合方法
A Combined Approach To Detect Key Variables In Thick Data Analytics
论文作者
论文摘要
在机器学习中，战略任务之一是仅选择重要变量作为响应的预测指标。在本文中，提出了一种方法，该方法包括在候选预测变量上应用置换测试，以仅确定最有用的变量。这种方法可能会受益一些工业问题，并提出了化学分析领域的应用。在提出的方法和套索之间进行了比较，这是文献中最常见的特征选择替代方案之一。
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.
