In this paper, we investigate dynamic feature selection within multivariate
time-series scenario, a common occurrence in clinical prediction monitoring
where each feature corresponds to a bio-test result. Many existing feature
selection methods fall short in effectively leveraging time-series information,
primarily because they are designed for static data. Our approach addresses
this limitation by enabling the selection of time-varying feature subsets for
each patient. Specifically, we employ reinforcement learning to optimize a
policy under maximum cost restrictions. The prediction model is subsequently
updated using synthetic data generated by trained policy. Our method can
seamlessly integrate with non-differentiable prediction models. We conducted
experiments on a sizable clinical dataset encompassing regression and
classification tasks. The results demonstrate that our approach outperforms
strong feature selection baselines, particularly when subjected to stringent
cost limitations. Code will be released once paper is accepted.

研究采用强化学习优化时间序列场景下的动态特征选择方法，提高临床预测监测中的数据挖掘效果。

医学预测监测中的动态特征选择：强化学习

Dynamic feature selection in medical predictive monitoring by  reinforcement learning

Dynamic feature selection, where we sequentially query features to make
accurate predictions with a minimal budget, is a promising paradigm to reduce
feature acquisition costs and provide transparency into the prediction process.
The problem is challenging, however, as it requires both making predictions
with arbitrary feature sets and learning a policy to identify the most valuable
selections. Here, we take an information-theoretic perspective and prioritize
features based on their mutual information with the response variable. The main
challenge is learning this selection policy, and we design a straightforward
new modeling approach that estimates the mutual information in a discriminative
rather than generative fashion. Building on our learning approach, we introduce
several further improvements: allowing variable feature budgets across samples,
enabling non-uniform costs between features, incorporating prior information,
and exploring modern architectures to handle partial input information. We find
that our method provides consistent gains over recent state-of-the-art methods
across a variety of datasets.

本文介绍了一种基于信息论的动态特征选择方法，通过学习选择策略实现特征效益的高效获取。与现有最先进方法相比，该方法在各种数据集上都提供了一致的收益。

动态特征选择的条件互信息估计

Estimating Conditional Mutual Information for Dynamic Feature Selection

To improve the performance in identifying the faults under strong noise for
rotating machinery, this paper presents a dynamic feature reconstruction signal
graph method, which plays the key role of the proposed end-to-end fault
diagnosis model. Specifically, the original mechanical signal is first
decomposed by wavelet packet decomposition (WPD) to obtain multiple subbands
including coefficient matrix. Then, with originally defined two feature
extraction factors MDD and DDD, a dynamic feature selection method based on L2
energy norm (DFSL) is proposed, which can dynamically select the feature
coefficient matrix of WPD based on the difference in the distribution of norm
energy, enabling each sub-signal to take adaptive signal reconstruction. Next
the coefficient matrices of the optimal feature sub-bands are reconstructed and
reorganized to obtain the feature signal graphs. Finally, deep features are
extracted from the feature signal graphs by 2D-Convolutional neural network
(2D-CNN). Experimental results on a public data platform of a bearing and our
laboratory platform of robot grinding show that this method is better than the
existing methods under different noise intensities.

本研究提出了一种动态特征重构信号图方法，采用小波包分解（WPD）和 2D-CNN 作为故障诊断模型，能够在高噪声下更好的实现旋转机械的故障诊断。

基于动态特征重构信号图的旋转机械故障识别

Fault Identification of Rotating Machinery Based on Dynamic Feature  Reconstruction Signal Graph

Feature selection helps reduce data acquisition costs in ML, but the standard
approach is to train models with static feature subsets. Here, we consider the
dynamic feature selection (DFS) problem where a model sequentially queries
features based on the presently available information. DFS is often addressed
with reinforcement learning (RL), but we explore a simpler approach of greedily
selecting features based on their conditional mutual information. This method
is theoretically appealing but requires oracle access to the data distribution,
so we develop a learning approach based on amortized optimization. The proposed
method is shown to recover the greedy policy when trained to optimality and
outperforms numerous existing feature selection methods in our experiments,
thus validating it as a simple but powerful approach for this problem.

提出了一种基于动态特征选择（DFS）和条件互信息的特征选择方法，该方法使用了一种基于分摊优化的学习方法，并在实验中表现出优于其他特征选择方法的性能。