We propose a novel reinforcement learning-based approach for adaptive and iterative feature selection. Given a masked vector of input features, a reinforcement learning agent iteratively selects certain features to be unmasked, and uses them to predict an outcome when it is sufficiently confident. The algorithm makes use of a novel environment setting, corresponding to a non-stationary Markov Decision Process. A key component of our approach is a guesser network, trained to predict the outcome from the selected features and parametrizing the reward function. Applying our method to a national survey dataset, we show that it not only outperforms strong baselines when requiring the prediction to be made based on a small number of input features, but is also highly more interpretable. Our code is publicly available at \url{https://github.com/ushaham/adaptiveFS}.

中文翻译：

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学习使用强化学习提出医学问题

我们提出了一种新的基于强化学习的自适应和迭代特征选择方法。给定输入特征的屏蔽向量，强化学习代理迭代地选择某些要取消屏蔽的特征，并在足够自信时使用它们来预测结果。该算法利用新的环境设置，对应于非平稳马尔可夫决策过程。我们方法的一个关键组成部分是猜测网络，经过训练以预测所选特征的结果并参数化奖励函数。将我们的方法应用于国家调查数据集，我们表明，当需要基于少量输入特征进行预测时，它不仅优于强基线，而且具有更高的可解释性。我们的代码可在 \url{https://github.com 上公开获得。