用SHAP导数和高斯镜像法，精准控制LSTM的误发现率。
CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors
- 用SHAP导数量化特征重要性，避免非线性干扰。
- 引入核独立度量，解决时序特征相关性问题。
- 提升模型可解释性，适合需要可信推理的时序任务。
我们提出CatNet，一种在LSTM中有效控制假发现率（FDR）并选择显著特征的算法。该方法利用SHAP值的导数量化特征重要性，并通过高斯镜像算法构建向量形式的镜像统计量以实现FDR控制。为克服特征间非线性或时序相关性带来的不稳定性，我们提出一种新的基于核的独立性度量。CatNet在模拟与真实数据上均表现稳健，减少过拟合，提升模型可解释性。该框架将SHAP用于FDR控制，并改进高斯镜像算法，可自然扩展至其他时间序列或序列深度学习模型。
原文摘要 · Abstract (English)
We introduce CatNet, an algorithm that effectively controls False Discovery Rate (FDR) and selects significant features in LSTM. CatNet employs the derivative of SHAP values to quantify the feature importance, and constructs a vector-formed mirror statistic for FDR control with the Gaussian Mirror algorithm. To avoid instability due to nonlinear or temporal correlations among features, we also propose a new kernel-based independence measure. CatNet performs robustly on different model settings with both simulated and real-world data, which reduces overfitting and improves interpretability of the model. Our framework that introduces SHAP for feature importance in FDR control algorithms and improves Gaussian Mirror can be naturally extended to other time-series or sequential deep learning models.
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