论文标题
在冗余的上下文中的顺序特征分类
Sequential Feature Classification in the Context of Redundancies
论文作者
论文摘要
全相关功能选择的问题与找到具有保留冗余的相关功能集有关。存在几个近似方法来解决这个问题，但只有一个可以区分强度和弱相关性。这种方法仅限于线性问题的情况。在这项工作中，我们通过使用随机森林模型和统计方法提出了一种在非线性情况下进行区分的新解决方案。
The problem of all-relevant feature selection is concerned with finding a relevant feature set with preserved redundancies. There exist several approximations to solve this problem but only one could give a distinction between strong and weak relevance. This approach was limited to the case of linear problems. In this work, we present a new solution for this distinction in the non-linear case through the use of random forest models and statistical methods.
