论文标题
使用同义持续方法对逐步特征选择的更强大和一般的选择性推断
More Powerful and General Selective Inference for Stepwise Feature Selection using the Homotopy Continuation Approach
论文作者
论文摘要
有条件的选择性推理（SI）已被积极研究为数据驱动假设的新统计推理框架。条件SI的基本思想是根据一组线性和/或二次不等式的选择事件进行推论。有条件的SI主要在特征选择的背景下进行了研究，例如逐步特征选择（SFS）。现有条件SI方法的主要局限性是由于过度调节而导致的功率损失，这是计算障碍所必需的。在这项研究中，我们使用同型方法为SF开发了一种更强大，更一般的条件SI方法，使我们能够克服这一限制。基于同质的SI对于更复杂的特征选择算法特别有效。例如，我们开发了一种有条件的SI方法，用于具有基于AIC的停止标准的前向SFS，并表明它不会受到算法复杂性增加的不利影响。我们进行了几项实验，以证明该方法的有效性和效率。
Conditional selective inference (SI) has been actively studied as a new statistical inference framework for data-driven hypotheses. The basic idea of conditional SI is to make inferences conditional on the selection event characterized by a set of linear and/or quadratic inequalities. Conditional SI has been mainly studied in the context of feature selection such as stepwise feature selection (SFS). The main limitation of the existing conditional SI methods is the loss of power due to over-conditioning, which is required for computational tractability. In this study, we develop a more powerful and general conditional SI method for SFS using the homotopy method which enables us to overcome this limitation. The homotopy-based SI is especially effective for more complicated feature selection algorithms. As an example, we develop a conditional SI method for forward-backward SFS with AIC-based stopping criteria and show that it is not adversely affected by the increased complexity of the algorithm. We conduct several experiments to demonstrate the effectiveness and efficiency of the proposed method.
