动态选择可测特征，应对特征集随实例变化的难题
Dynamic Feature Selection from Variable Feature Sets Using Features of Features
- 利用特征的元信息（特征的特征）指导选择
- 在特征集变动时仍能有效选中关键特征
- 适合测量成本高且特征可用性不固定的场景
机器学习模型通常假设用于输出的特征集是预先固定的。但在许多实际问题中，测量特征会产生成本。为降低测量成本，已有方法提出动态选择需测量的特征，但这些方法均假设可测量特征集恒定，难以应对特征集随实例变化的情况。为此，本文定义了具有可变特征集的动态特征选择（DFS）新问题，并提出一种深度学习方法，利用每项特征的先验信息（即“特征的特征”）。在多个数据集上的实验表明，该方法能有效基于先验信息进行特征选择，即使在可测量特征集随实例变化时也表现良好。
原文摘要 · Abstract (English)
Machine learning models usually assume that a set of feature values used to obtain an output is fixed in advance. However, in many real-world problems, a cost is associated with measuring these features. To address the issue of reducing measurement costs, various methods have been proposed to dynamically select which features to measure, but existing methods assume that the set of measurable features remains constant, which makes them unsuitable for cases where the set of measurable features varies from instance to instance. To overcome this limitation, we define a new problem setting for Dynamic Feature Selection (DFS) with variable feature sets and propose a deep learning method that utilizes prior information about each feature, referred to as ''features of features''. Experimental results on several datasets demonstrate that the proposed method effectively selects features based on the prior information, even when the set of measurable features changes from instance to instance.
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