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Post-Hoc Feature Selection Layer for Neural Networks Interpretability

João Kenji Suwa, João Pedro Silveira e Silva, Bruno Iochins Grisci · 2026

DOI: 10.1109/ijcnn69854.2026.11729834 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

複雑なニューラルネットワークの解釈性は依然として重要な課題であり、特にすでに高ストックドメインで展開されているモデルにとっては重要な課題である。これに対処するために、我々は機能選択層(FSL)を改良して、トレーニング後の機能重量を最適化するために、我々はポストホックFSLと呼ぶバージョンである。我々のアプローチは、FSLを、既に凍結されたプレトレーニングモデルやその学習パラメータを変更することなく、既に凍結されたプレトレーニングモデルと統合する軽量のモジュールとして再編成する。このポストホックFSLは、オリジナルモデルの学習出力に基づいてその重量を細かく調整することによって、関連性を学習

Machine translation (Meta M2M100); the English original follows.

Abstract

The interpretability of complex neural networks remains a critical challenge, especially for models already deployed in high-stakes domains. To address this, we adapted the Feature Selection Layer (FSL) to optimize feature weights post-training, a variant we term post-hoc FSL. Our approach reframes the FSL as a lightweight, trainable module that integrates with already frozen pre-trained models on tabular datasets to highlight the features the original model considers most important. This post-hoc FSL learns feature relevance by fine-tuning its weights based on the original model's learned outputs. Crucially, this process is non-invasive, operating without altering the original model's architecture or its learned parameters. We conducted our experiments using both statistical and visual metrics, including accuracy, F1 score, recall, precision, weighted t-SNE and silhouette score, and also analyzed the stability of the post-hoc FSL on high-dimensional synthetic and real-world tabular datasets. We compared the post-hoc FSL feature weighting method using these metrics against the original embedded FSL and other post-hoc interpretability methods, such as Integrated Gradients, Noise Tunnel, DeepLIFT, Gradient SHAP, and Feature Ablation. Experimental results demonstrate that the post-hoc FSL feature weighting method successfully identified relevant features across the different datasets, maintaining the predictive power of the original neural network while enhancing its interpretability.

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Topics: Interpretability, Artificial intelligence, Computer science, Artificial neural network, Feature selection, Layer (electronics)

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