标题： AcSel：在相关数据集中选择变量的准确性 显示英文标题

标题： AcSel: selecting variables with accuracy in correlated datasets

摘要： 随着高通量技术的出现，可以以相对较低的成本测量大量数据。 这种情况在从科学到人文学科的许多领域中都会出现，变量选择可能对回答每个领域特有的挑战有很大帮助。 变量选择可以帮助了解所有测量变量中哪些是相关的，哪些不是。 已经提出了很多方法来处理这个问题，Lasso和其他惩罚回归是其中的特例。 这些方法在某些情况下会失败，解释变量之间的线性相关性是最常见的情况，尤其是在大数据集中。 在本文中，我们介绍了AcSel，一种能够提高任何变量选择方法准确性的包装算法。 为了达到这个结果，我们使用了大量的计算模拟。 显示英文摘要

摘要： With the emergence of high-throughput technologies, it is possible to measure large amounts of data relatively at low cost. Such situations arise in many fields from sciences to humanities, and variable selection may be of great help to answer challenges that are specific to each of them. Variable selection may allow to know, among all measured variables, which are of interest and which are not. A lot of methods have been proposed to handle this issue, with the Lasso and other penalized regression as special cases. These methods fail in some cases and linear correlation between explanatory variables is the most common of these, especially in big datasets. In this article, we introduce AcSel, a wrapping algorithm able to enhance the accuracy of any variable selection method. To achieve this result, we use intensive computational simulations.