CatNet paper introduces SHAP for feature importance in LSTM FDR control

Researchers have introduced CatNet, a novel algorithm designed to control the False Discovery Rate (FDR) and identify significant features within Long Short-Term Memory (LSTM) networks. This method utilizes the derivative of SHAP values to assess feature importance and employs the Gaussian Mirror algorithm for FDR control. CatNet also incorporates a new kernel-based independence measure to handle complex correlations among features, demonstrating robust performance on both simulated and real-world data to enhance model interpretability and reduce overfitting. AI

影响 Introduces a new method for improving the interpretability and robustness of sequential deep learning models.

排序理由 This is a research paper detailing a new algorithm for feature selection and FDR control in LSTMs. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• CatNet
• Gaussian Mirror
• Jiaan Han
• LSTM
• SHAP

• 论文
• 其他

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