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CatNet paper introduces SHAP for feature importance in LSTM FDR control

By PulseAugur Editorial · [1 sources] ·

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

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

RANK_REASON 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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

CatNet paper introduces SHAP for feature importance in LSTM FDR control

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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]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiaan Han, Junxiao Chen, Yanzhe Fu ·

    CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors

    arXiv:2411.16666v4 Announce Type: replace-cross Abstract: 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 construc…

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