论文标题
E2E-FS：神经网络的端到端特征选择方法
E2E-FS: An End-to-End Feature Selection Method for Neural Networks
论文作者
论文摘要
经典的嵌入式特征选择算法通常分为两个大组：基于树的算法和套索变体。两种方法都集中在不同方面：虽然基于树的算法提供了一个明确的解释，即使用哪些变量用于触发某些输出，但类似Lasso的方法牺牲了详细的解释，以提高其准确性。在本文中，我们提出了一种新颖的嵌入式特征选择算法，称为端到端特征选择（E2E-FS），旨在以巧妙的方式提供准确性和解释性。尽管具有非凸正则化术语，但我们的算法（类似于LASSO方法）通过梯度下降技术解决，引入了一些限制，这些限制迫使该模型专门选择了分类器随后将使用的最大数量。尽管这些是硬限制，但获得的实验结果表明，该算法可以与使用梯度下降算法训练的任何学习模型一起使用。
Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and lasso variants. Both approaches are focused in different aspects: while the tree-based algorithms provide a clear explanation about which variables are being used to trigger a certain output, lasso-like approaches sacrifice a detailed explanation in favor of increasing its accuracy. In this paper, we present a novel embedded feature selection algorithm, called End-to-End Feature Selection (E2E-FS), that aims to provide both accuracy and explainability in a clever way. Despite having non-convex regularization terms, our algorithm, similar to the lasso approach, is solved with gradient descent techniques, introducing some restrictions that force the model to specifically select a maximum number of features that are going to be used subsequently by the classifier. Although these are hard restrictions, the experimental results obtained show that this algorithm can be used with any learning model that is trained using a gradient descent algorithm.
